Occupancy Interaction Detection

RELATED APPLICATIONS

0001This patent application is a continuation-in-part (CIP) of U.S. patent application Ser. No. 14/183,747, filed Feb. 19, 2014, which is herein incorporated by reference.

FIELD OF THE EMBODIMENTS

0002The described embodiments relate generally to environmental control systems. More particularly, the described embodiments relate to methods, apparatuses and systems for tracking motion which can be used for occupancy interaction detection.

BACKGROUND

0003Building control systems exist for monitoring occupancy and energy usage. The building control systems may provide energy management and control based on sensed occupancy.

0004Research has shown that increased customer engagement, (where store associates help customers in a retail store) results in higher sales. Therefore retail establishments desire to increase customer engagement and track customer engagement by measuring the interactions, and tracking the customer engagement.

0005It is desirable to have a method, system and apparatus for tracking motion of an area by a building control system for occupancy interaction detection.

SUMMARY

0006An embodiment includes an occupancy interaction detection system. The occupancy interaction detection system includes a plurality of sensors located within an area, the plurality of sensors operative to sense at least motion of a first occupant and a second occupant of the area, and communication links between each of the sensors and a controller. For an embodiment, the controller operative to receive sense data from the plurality of sensors, track locations of the first occupant of the area based on the sensed motion of the first occupant, track locations of the second occupant of the area based on the sensed motion of the second occupant, and identify an interaction between the first occupant and the second occupant, comprising detecting the first occupant to be within a threshold distance of the second occupant.

0007Another embodiment includes a method of occupancy interaction detection. The method includes sensing at least motion of a first occupant and a second occupant of the area, tracking locations of the first occupant of the area based on the sensed motion of the first occupant, tracking locations of the second occupant of the area based on the sensed motion of the second occupant, and identifying an interaction between the first occupant and the second occupant, comprising detecting the first occupant to be within a threshold distance of the second occupant.

0008Other aspects and advantages of the described embodiments will become apparent from the following detailed description, taken in conjunction with the accompanying drawings, illustrating by way of example the principles of the described embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

0009<figref idref="DRAWINGS">FIG. 1</figref> shows an area that includes multiple rooms, wherein sensors within each of the multiple rooms and a controller are utilized for tracking motion and detecting occupancy interaction, according to an embodiment.

0010<figref idref="DRAWINGS">FIG. 2</figref> shows an area that includes multiple rooms, wherein sensors within each of the multiple rooms and a controller are utilized for tracking motion and detecting occupancy interaction, according to another embodiment.

0011<figref idref="DRAWINGS">FIG. 3</figref> shows an area that includes multiple rooms, wherein sensors within each of the multiple rooms and a controller are utilized for tracking motion and detecting occupancy interaction, according to another embodiment.

0012<figref idref="DRAWINGS">FIG. 4</figref> is a flow chart that includes steps of a method of motion tracking and occupancy interaction detection, according to an embodiment.

0013<figref idref="DRAWINGS">FIG. 5</figref> shows an area that includes multiple rooms, wherein sensors within each of the multiple rooms and a controller are utilized for tracking motion and occupancy interaction detection, according to another embodiment.

0014<figref idref="DRAWINGS">FIG. 6</figref> shows a sensor and associated lighting control, according to an embodiment.

0015<figref idref="DRAWINGS">FIG. 7</figref> shows multiple sensors, including specific sensors and neighboring sensors of the specific sensors, according to an embodiment.

0016<figref idref="DRAWINGS">FIG. 8</figref> shows invalid groups of sensors, according to an embodiment.

0017<figref idref="DRAWINGS">FIG. 9</figref> shows a group of sensors being tracked over multiple frames, according to an embodiment.

0018<figref idref="DRAWINGS">FIG. 10</figref> shows an initially identified group of sensors being split, according to an embodiment.

0019<figref idref="DRAWINGS">FIG. 11</figref> shows an initially identified group of sensors being split, and two separate trails being formed, according to an embodiment.

0020<figref idref="DRAWINGS">FIG. 12</figref> shows an obstruction that includes a doorway, and formation of the group, according to an embodiment.

0021<figref idref="DRAWINGS">FIG. 13</figref> is a flow chart that includes steps of a method of tracking motion, according to another embodiment.

DETAILED DESCRIPTION

0022As shown in the drawings, the described embodiments provide methods, apparatuses, and systems for tracking motion and occupancy interaction detection. For at least some embodiments, the occupancy interaction includes a customer engagement. Further, for at least some embodiments, the effectiveness of the customer engagement is estimated.

0023<figref idref="DRAWINGS">FIG. 1</figref> shows an area that includes multiple rooms <b>140</b>, <b>150</b>, <b>160</b>, <b>170</b>, wherein an array of sensors (such as, sensors <b>110</b> or <b>112</b>) within each of the multiple rooms <b>140</b>, <b>150</b>, <b>160</b>, <b>170</b> and a controller <b>190</b> are utilized for tracking motion and detecting occupant interaction, according to an embodiment. For at least some embodiments, communication links are established between each of the sensors and the controller <b>190</b>. These links can be wired links, or wireless links. As shown, occupancy of a first occupant (occupant<b>1</b>) is sensed at a first time (time<b>1</b>). Further, for an embodiment, occupancy of a second occupant (occupant<b>2</b>) is also sensed at the first time (time<b>1</b>). Note that while occupancy of the first and second occupants are shown and described as occurring at the first time (time<b>1</b>), it is to be understood that for other embodiments the occupancy and motion detection of the different occupants does not have to occur at exactly the same time. The timing of the occupancy and motion sensing just needs to be able to sense the proximity of the occupants with respect to each other, and as will be described, sense the occupants being within a threshold distance from each other.

0024Further, motion of the occupants is sensed by the array of sensors according to described embodiments of occupant motion sensing. The motion sensing allows for tracking of the locations of the occupants. As shown, each occupant (occupant<b>1</b>, occupant<b>2</b>) generates a motion trial (trial <b>111</b> for occupant<b>1</b> and trail <b>113</b> for occupant<b>2</b>) that depict successive estimated locations of each of the occupants over time.

0025Detecting an Interaction

0026For at least some embodiments, an occupancy interaction is detected with the occupants (occupant<b>1</b> and occupant<b>2</b>) are sensed to be within a threshold distance of each other. That is, for an embodiment, sense data is received from the plurality of sensors. The locations of the first occupant of the area are tracked based on the sensed motion of the first occupant. The locations of the second occupant of the area are tracked based on the sensed motion of the second occupant. An interaction between the first occupant and the second occupant is identified by detecting the first occupant to be within a threshold distance of the second occupant. For various embodiments, the threshold distance is adaptively selected. That is, some types of interaction can be established by varying threshold distances. Further, for at least some embodiments, an interaction may be established after the occurrence based on tracked motion of the occupant after the interaction. That is, based on a later action of an occupant, historical analysis of the behaviors of the occupants, and past proximity (relative to each other) can be used to establish that an interaction occurred. Further, the threshold distance may be adaptively selected based on this historical analysis.

0027For at least some embodiments, identifying the interaction further includes detecting the first occupant to be within the threshold distance of the second occupant for greater than a threshold period of time. That is, as shown in <figref idref="DRAWINGS">FIG. 1</figref>, the threshold distance may be identified by the region <b>120</b>. For an embodiment, determination of an interaction between the first occupant (occupant<b>1</b>) and the second occupant (occupant<b>2</b>) is determined by the occupants being within the threshold distance of each other. As shown in <figref idref="DRAWINGS">FIG. 1</figref>, this occurs at a second time (time<b>2</b>). For an embodiment, determination of an interaction between the first occupant (occupant<b>1</b>) and the second occupant (occupant<b>2</b>) is determined by the occupants not only being within the threshold distance of each other, but also determining the occupants to be within the threshold distance of each other for greater than the threshold period of time. In some instances, the threshold period of time confirms that the occupants are really interacting, and not just merely passing by each other. For at least some embodiments, the threshold period of time is adaptively selected. For example, if one occupant is a sales associate, and the other occupant is a customer, the threshold period of time can provide an indication of customer engagement. However, the threshold period of time can be selected based upon the type of product or service being sold. Further, other applications of occupant interaction may dictate other threshold periods of time.

0028For at least some embodiments, the timing a length of the identified interaction is timed. That is, for example, a threshold period of time may have been exceeded, but the timing of the interaction is further timed for other reasons. For example, such timing of the interaction may be used to determine how effective a particular sales associate is in customer engagements. A shorter effective engagement may be better than a longer engagement.

0029At least some embodiments further include utilizing an occupant identifier to detect an identifier of the first occupant or the second occupant. For example, the sensors may include motion sensors, but further support wireless communication. For example, for an embodiment, the sensors support an identifier that is either transmitted or read (e.g. Bluetooth beacon, RFID, QR or Bar Codes) communications, which is recognized by an overhead sensor (Bluetooth, RFID Reader, Video Camera). For an embodiment, one of the sensed occupants is carrying a mobile wireless device that supports Bluetooth or some other type of wireless communications. For an embodiment, the wireless device of the user transmits an identifier within, for example, a beacon that is received by one or more of the sensors. As such, the receiving sensor (Bluetooth, RFID, Video Camera), and therefore, the controller are able to identify the occupant.

0030At least some embodiments further include generating a profile of at least one of the first occupant and the second occupant based on identification of the at least one of the first occupant and the second occupant, and tracked motion of the at least one of the first occupant and the second occupant. The profile can include any number of useful pieces of identified and/or tracked information or behavior. For the profile may be generated for an identified sales associate. The profile may include one or more locations, a number of interactions, an effectiveness (measured by subsequent trails (tracked motion and locations after sensing an interaction or engagement)), or purchasing decisions).

0031As previously stated, at least some embodiments include detecting a customer engagement. For an embodiment, detecting a customer engagement includes the controller being further operative to associate the identifier of the first occupant or the second occupant with a sales associate, and detect the customer engagement based on the identified interaction between the first occupant an the second occupant, and the association of the sales associate with the identifier of the first occupant or the second occupant.

0032Tracking Motion of Occupants after an Interaction

0033<figref idref="DRAWINGS">FIG. 2</figref> shows an area that includes multiple rooms <b>140</b>, <b>150</b>, <b>160</b>, <b>170</b>, wherein an array of sensors (such as, sensors <b>110</b> or <b>112</b>) within each of the multiple rooms <b>140</b>, <b>150</b>, <b>160</b>, <b>170</b> and a controller <b>190</b> are utilized for tracking motion and detecting occupant interaction, according to another embodiment. This embodiment includes further tracking motion and location (<b>115</b>, <b>117</b>) of the occupants (occupant<b>1</b> at time<b>3</b> and occupant<b>2</b> at time<b>3</b>) after detecting the occupant interaction. The tracking of the occupants after detecting the occupant interaction can be very useful in determining what influence the occupant interaction had upon either of the occupants.

0034At least some embodiments include identifying an influence of the identified interaction on at least the first occupant of the second occupant based on the tracking of motion of at least the first occupant of the second occupant after identifying the interaction.

0035For at least some embodiments, at least one of the first occupant or the second occupant is determined to be a customer, and wherein the controller is further operative to track motion of the customer, and determine whether the customer completes a transaction. A transaction completion can be identified by the customer engaging with a cashier or be detecting a completed sale.

0036At least some embodiments include the controller being further operative to generate engagement metrics that measure how effective multiple different customer engagements are in completion of a transaction. For an embodiment, one or more metrics are generated for one or more of a plurality of identified sales associates. For an embodiment, the metrics include a number of identified customer engagements by the sales associate over a defined period of time. For an embodiment, the metrics include an average, a minimum, and/or a maximum time duration of customer engagements. For an embodiment, the metrics include determining a number of engagements that result in a sale. For an embodiment, the metrics include an average or other statistical representation of a value of sales after sensing a customer engagement. For an embodiment, the metrics are utilized to rank or determined the effectiveness of the sales associates.

0037Multiple Occupants

0038<figref idref="DRAWINGS">FIG. 3</figref> shows an area that includes multiple rooms <b>140</b>, <b>150</b>, <b>160</b>, <b>170</b>, wherein an array of sensors (such as, sensors <b>110</b> or <b>112</b>) within each of the multiple rooms <b>140</b>, <b>150</b>, <b>160</b>, <b>170</b> and a controller <b>190</b> are utilized for tracking motion and detecting occupant interaction, according to another embodiment. This embodiment includes further tracking motion and location of a third occupant (occupant<b>3</b>). Further, an occupant interaction <b>320</b> is identified in which greater than two occupants (occupant<b>1</b>, occupant<b>2</b>, occupant<b>3</b>) interact. That is, for an embodiment, the plurality of sensors are further operative to sense at least motion of a third occupant of the area, and the controller is further operative to track locations of the third occupant of the area, and identify an interaction between the first occupant, the second occupant, and the third occupant, comprising detecting the first occupant, the second occupant and the third occupant to be within a threshold distance of each other. While shown as three occupants in the occupant interaction, any number of occupants can be included within an occupant interaction.

0039For at least some embodiments, customer engagements of multiple sales associates are tracked. For an embodiment, multiple sales associates simultaneously engaging with a single customer is tracked, thereby providing an alert of a potentially inefficient engagement. For an embodiment, congregations of sales associates are tracked, thereby providing an alert of a potentially inefficient engagement.

0040<figref idref="DRAWINGS">FIG. 4</figref> is a flow chart that includes steps of a method of motion tracking and occupancy interaction detection, according to an embodiment. A first step <b>410</b> includes sensing at least motion of a first occupant and a second occupant of the area. A second step <b>420</b> includes tracking locations of the first occupant of the area based on the sensed motion of the first occupant. A third step <b>430</b> includes tracking locations of the second occupant of the area based on the sensed motion of the second occupant. A fourth step <b>440</b> includes identifying an interaction between the first occupant and the second occupant, comprising detecting the first occupant to be within a threshold distance of the second occupant.

0041As previously described, at least some embodiments further include timing a length of the identified interaction.

0042As previously described, for at least some embodiments the plurality of sensors further sense at least motion of a third occupant of the area, and further including identifying an interaction between the first occupant, the second occupant, and the third occupant by detecting the first occupant, the second occupant and the third occupant to be within a threshold distance of each other.

0043As previously described, at least some embodiments further include tracking motion of at least the first occupant of the second occupant after identifying the interaction. As previously described, at least some embodiments further include identifying an influence of the identified interaction on at least the first occupant of the second occupant based on the tracking of motion of at least the first occupant of the second occupant after identifying the interaction.

0044As previously described, for at least some embodiments the plurality of sensors includes an occupant identifier that detects an identifier of the first occupant or the second occupant. As previously described, at least some embodiments further include generating a profile of at least one of the first occupant and the second occupant based on identification of the at least one of the first occupant and the second occupant, and tracked motion of the at least one of the first occupant and the second occupant. As previously described, at least some embodiments further include detecting a customer engagement, including associating the identifier of the first occupant or the second occupant with a sales associate, and detecting the customer engagement based on the identified interaction between the first occupant an the second occupant, and the association of the sales associate with the identifier of the first occupant or the second occupant. As previously described, for at least some embodiments at least one of the first occupant or the second occupant is determined to be a customer, and the motion of the customer is tracked, and whether the customer completes a transaction is determined. As previously described, at least some embodiments further include generating engagement metrics that measure how effective multiple different customer engagements are in completion of a transaction.

0045As previously described, at least some embodiments further include identifying a group of sensors that includes a plurality of neighboring sensors sensing motion greater than a motion threshold during a time interval, and tracking motion including linking the group to at least one past group of at least one past time interval. As previously described, for at least some embodiments the tracked motion of the group includes at least one of the plurality of neighboring sensors within the group being different than a plurality of sensors of the at least one past group.

0046As previously described, for at least some embodiments further include grouping the sense data according to identified groupings of the plurality of sensors, and sensing motion of the occupants within one or more of the groups based on the data analytics processing of the groups of sensed data. For at least some embodiments, the data analytics processing includes pattern recognition processing. At least some embodiments further include sensing numbers of occupants within one or more of the groups based on the data analytics processing of the groups of sensed data. For at least some embodiments, at least a portion of the plurality of sensors includes motion sensors, and sensing numbers of occupants within one or more of the groups based on the data analytics processing of the groups of sensed data includes grouping motion sensing data according to one or more identified rooms within the area, performing the data analytics processing once every sampling period, and performing the data analytics processing on the motion sensing data to determine a number of occupants within the one or more identified rooms, and a level of certainty of the number of occupants.

0047For an embodiment, a plurality of sensors of an area or structure, are monitored over multiple time intervals. For each time interval, groups of sensor are identified that sense motion of greater than a threshold for the time interval, thereby indicating the presence of, for example, an occupant. A group of a time interval is linked with other groups of different time intervals, thereby indicated motion. The motion is tracked across multiple time intervals, thereby tracking motion within the area or structure.

0048The aggregation of the sensor data over time provides valuable insights for parties interested in optimizing space utilization and planning the construction of future spaces. This aggregation can be used to detect abnormalities in real time operation of, for example, an office building.

0049<figref idref="DRAWINGS">FIG. 5</figref> shows an area that includes multiple rooms <b>540</b>, <b>550</b>, <b>560</b>, <b>570</b>, wherein sensors (such as, sensor <b>510</b> or <b>512</b>) within each of the multiple rooms <b>540</b>, <b>550</b>, <b>560</b>, <b>570</b> and a controller <b>590</b> are utilized for tracking motion, according to an embodiment. Groups of sensors are identified for each of different time intervals <b>501</b>, <b>502</b>, <b>503</b>, <b>504</b>, <b>505</b> based on motion sensed by the sensors, and by the locations of the sensors.

0050For the first time interval <b>501</b>, a sensor (such as, sensor <b>510</b>) senses motion and generating a sense signal that indicates sensed motion of greater than a predetermined threshold. Neighboring sensors (such as, sensor <b>512</b>) that also sense motion greater than the predetermined threshold or greater than a second predetermined threshold are included within the group of the first time interval <b>501</b>. As will be described, for an embodiment, neighboring sensors are identified based upon prior knowledge of the locations of the sensors. For an embodiment, neighboring sensors are sensors having a location within a distance threshold of each other. However, for at least some embodiments, the criteria for determining or selecting neighbor sensors is not based on entirely based upon distances between sensors. At least some embodiments additionally or alternatively account for sensor spacing, other sensors in the area, and/or obstructions. Additionally, the distance threshold does not have to be preselected. For an exemplary embodiment, the distance threshold is selected to be two times the average distance to the second closest sensor (that is, the sensor second closest to initially sensing sensor). Generally, the distance threshold is preselected. For an embodiment, neighboring sensors are predetermined based on the prior location knowledge of the sensors.

0051For the second time interval <b>502</b>, a sensor is again identified that sensed motion greater than a threshold. Again, a group is then determined. For at least some embodiments, motion is sensed by linking a group of the first time interval <b>501</b> with a group of the second time interval. For an embodiment, the linking is determined by the proximity of the sensors within the different groups. For an embodiment, the linking is established by a commonality of at least one sensor. For an embodiment, the linking is based on identifying neighboring sensors of the different groups. For an embodiment, the linking is based on identifying neighboring sensors that are also neighbors of neighboring sensors of the different groups.

0052The motion tracking also includes identifying groups that physically cannot exist, and motion of groups that physically cannot occur. For example, a group <b>508</b> can be determined for a time interval. However, due to the existence of a wall <b>520</b> within the group, physically, the group cannot actually exist, and the group is then determined to not be valid.

0053As shown, the motion tracking of the different time intervals <b>501</b>, <b>502</b>, <b>503</b>, <b>504</b>, <b>505</b> shows motion from the first room <b>540</b>, to the second room <b>550</b>, and to the third room <b>560</b>. This motion physically is acceptable because the group motion passes through, for example, doorways (such as, doorway <b>530</b>). However, such group motion would not be determined to be valid if the motion passed, for example, through a barrier, such as, a wall <b>520</b>.

0054As shown, for an embodiment, a controller is electronically interfaced with a controller <b>590</b>. For at least some embodiments, the controller <b>590</b> is operable to processed sensed sensor information to monitor motion of groups, and thereby, sense motion. While shown as a standalone controller, it is to be understood that for an embodiment, each of the sensors include controllers, and the sensed information processing can be performed by any combination of one of more of the sensor controllers. That is, the sensed information processing can be centralized, or de-centralized across the controllers of the sensors.

0055For an embodiment, communication links are established between each of the sensors and the controller <b>590</b>. For an embodiment, the sensors are directly linked to the controller <b>590</b>. For another embodiment, at least some of the sensors are linked to the controller <b>590</b> through other sensors. For an embodiment, the sensors form a wireless mesh network that operates to wirelessly connect (link) each of the sensors to the controller.

0056Regardless of the location or configuration of the controller <b>590</b>, for an embodiment, the controller <b>590</b> is operative to receive sense data from the plurality of sensors, group the data according to identified groupings of the plurality of sensors, and track motion within at least a portion of the area based on data analytics processing of one or more of the groups of sensed data.

0057<figref idref="DRAWINGS">FIG. 6</figref> shows sensor and associated lighting control, according to an embodiment. For an embodiment, the sensors described include a smart sensor system <b>602</b>. Further, a lighting control system <b>600</b> includes the smart sensor system <b>602</b> that is interfaced with a high-voltage manager <b>604</b>, which is interfaced with a luminaire <b>646</b>. The sensor and associated lighting control of <figref idref="DRAWINGS">FIG. 6</figref> is one exemplary embodiment of the sensors utilized for tracking motion. Many different sensor embodiments are adapted to utilization of the described embodiments for tracking motion. For at least some embodiments, sensors that are not directly associated with light control are utilized.

0058The motion tracking of the described embodiments can be utilized for optimal control of lighting and other environmental controls of an area or structure that utilizes the motion tracking. The control can be configured to save energy and provide comfort to occupants of the area or structure.

0059The high-voltage manager <b>604</b> includes a controller (manager CPU) <b>620</b> that is coupled to the luminaire <b>646</b>, and to a smart sensor CPU <b>635</b> of the smart sensor system <b>602</b>. As shown, the smart sensor CPU <b>645</b> is coupled to a communication interface <b>650</b>, wherein the communication interface <b>650</b> couples the controller to an external device. The smart sensor system <b>602</b> additionally includes a sensor <b>640</b>. As indicated, the sensor <b>640</b> can include one or more of a light sensor <b>641</b>, a motion sensor <b>642</b>, and temperature sensor <b>643</b>, and camera <b>644</b> and/or an air quality sensor <b>645</b>. It is to be understood that this is not an exhaustive list of sensors. That is additional or alternate sensors can be utilized for occupancy and motion detection of a structure that utilizes the lighting control sub-system <b>600</b>. The sensor <b>640</b> is coupled to the smart sensor CPU <b>645</b>, and the sensor <b>640</b> generates a sensed input. For at least one embodiment, at least one of the sensors is utilized for communication with the user device.

0060For an embodiment, the temperature sensor <b>643</b> is utilized for motion tracking. For an embodiment, the temperature sensor <b>643</b> is utilized to determine how much and/or how quickly the temperature in the room has increased since the start of, for example, a meeting of occupants. How much the temperate has increased and how quickly the temperature has increased can be correlated with the number of the occupants. All of this is dependent on the dimensions of the room and related to previous occupied periods. For at least some embodiment, estimates and/or knowledge of the number of occupants within a room are used to adjust the HVAC (heating, ventilation and air conditioning) of the room. For an embodiment, the temperature of the room is adjusted based on the estimated number of occupants in the room.

0061According to at least some embodiments, the controllers (manager CPU <b>620</b> and the smart sensor CPU) are operative to control a light output of the luminaire <b>646</b> based at least in part on the sensed input, and communicate at least one of state or sensed information to the external device.

0062For at least some embodiments, the high-voltage manager <b>604</b> receives the high-power voltage and generates power control for the luminaire <b>646</b>, and generates a low-voltage supply for the smart sensor system <b>602</b>. As suggested, the high-voltage manager <b>604</b> and the smart sensor system <b>602</b> interact to control a light output of the luminaire <b>646</b> based at least in part on the sensed input, and communicate at least one of state or sensed information to the external device. The high-voltage manager <b>604</b> and the smart sensor system <b>602</b> can also receive state or control information from the external device, which can influence the control of the light output of the luminaire <b>646</b>. While the manager CPU <b>620</b> of the high-voltage manager <b>604</b> and the smart sensor CPU <b>645</b> of the smart sensor system <b>602</b> are shown as separate controllers, it is to be understood that for at least some embodiments the two separate controllers (CPUs) <b>620</b>, <b>645</b> can be implemented as single controller or CPU.

0063For at least some embodiments, the communication interface <b>650</b> provides a wireless link to external devices (for example, the central controller, the user device and/or other lighting sub-systems or devices).

0064An embodiment of the high-voltage manager <b>604</b> of the lighting control sub-system <b>600</b> further includes an energy meter (also referred to as a power monitoring unit), which receives the electrical power of the lighting control sub-system <b>600</b>. The energy meter measures and monitors the power being dissipated by the lighting control sub-system <b>600</b>. For at least some embodiments, the monitoring of the dissipated power provides for precise monitoring of the dissipated power. Therefore, if the manager CPU <b>620</b> receives a demand response (typically, a request from a power company that is received during periods of high power demands) from, for example, a power company, the manager CPU <b>620</b> can determine how well the lighting control sub-system <b>600</b> is responding to the received demand response. Additionally, or alternatively, the manager CPU <b>620</b> can provide indications of how much energy (power) is being used, or saved.

0065<figref idref="DRAWINGS">FIG. 7</figref> shows multiple sensors, including specific sensors and neighboring sensors of the specific sensors, according to an embodiment. As shown, a specific sensor S<b>1</b> is identified. For an embodiment, location information of the sensors is utilized to identify neighboring sensors. The neighbor sensor determinations can be predetermined, or adaptively adjusted and selected based on the type of motion being tracked. For an embodiment, the neighboring sensors are preselected (chosen beforehand) and a not changed after being selected. For an embodiment, a neighboring status of two previously selected neighboring sensors can be updated or changed if the motion sensing patterns are not reflected by the motion sensing patterns of typical neighboring sensors.

0066As shown in <figref idref="DRAWINGS">FIG. 7</figref>, sensors S<b>2</b>, S<b>3</b>, S<b>4</b>, S<b>5</b> are neighboring sensor of sensor S<b>1</b>. Therefore, when sensor S<b>1</b> is determined to have sensed motion greater than a threshold, the neighboring sensors S<b>2</b>, S<b>3</b>, S<b>4</b>, S<b>5</b> are checked to determine whether they sensed motion of greater than the threshold, or greater than a second threshold during the same time interval. For an embodiment, the neighboring sensors that do sense motion of greater than the threshold, or greater than a second threshold during the same time interval are included within a group established by the motion sensing of the sensor S<b>1</b>.

0067Further, as shown in <figref idref="DRAWINGS">FIG. 7</figref>, other proximate sensors are determined not to be neighboring sensor of S<b>1</b>. For an embodiment, sensors S<b>6</b>, S<b>7</b>, S<b>8</b>, S<b>9</b>, S<b>10</b>, S<b>11</b> are not neighboring sensor because, for example, they are located more than a threshold distance away from the sensor S<b>1</b>.

0068As previously described, additional or alternate criteria can be used for the determination or selection of which sensor are designated as neighboring sensors other than distance alone. The neighboring sensor determinations can also take into account sensor spacing, other sensors in the area, as well as walls and obstructions. The distance threshold for determining neighboring sensors does not have to be preselected. For an exemplary embodiment, the distance threshold is two times the average distance between a sensor and its second closest sensor.

0069For example, the sensor S<b>10</b> of <figref idref="DRAWINGS">FIG. 7</figref> could be selected as a neighboring sensor of S<b>11</b> because there is no sensor located directly above sensor S<b>3</b>. However, if there was a sensor location just above S<b>3</b>, then sensor S<b>10</b> and sensor S<b>11</b> would not be selected as neighboring sensors.

0070<figref idref="DRAWINGS">FIG. 8</figref> shows invalid groups of sensors, according to an embodiment. As described, groups of sensor are identified over time intervals, and motion is tracked by linking the groups of the different time intervals. At least some embodiments include identifying groups that are initially selected, and then the shape of the group is refined (that is, modifying the sensors that are included within the group). For an embodiment, refining the shape of the group include identifying shape of the group that are not allowed for a valid group. For at least some embodiments, this includes identifying invalid shapes, and the changing the sensors included within the group to make the group into a valid or acceptable shape, or ignoring the motion tracking of the group.

0071<figref idref="DRAWINGS">FIG. 8</figref> shows some exemplary invalid shapes. A first shape “A” includes a shape in the form of a “C”. This shape of this group includes an interior sensor <b>810</b> which is not allowable. Therefore, the shape of this group is to be modified to eliminate the interior sensors.

0072A second shape “B” includes a shape in the form of a “O”. This shape of this group includes an interior sensor <b>830</b> which is not allowable. Therefore, the shape of this group is to be modified to eliminate the interior sensors.

0073For an embodiment, an interior sensor is a non-active sensor (that is, a sensor that does not sense motion of greater than a motion threshold), wherein an angular separation between neighboring active sensors of the group is less than a threshold amount. For a specific embodiment, the threshold amount is approximately 135 degrees. This relationship hold true for both of the interior sensors of the first shape and second shape described above. Therefore, an embodiment includes invalidating a group of sensors of an internal non-active sensor has an angular separation between active sensors within the group of less than a threshold angle or amount. The sensors of the group are reselected to eliminate the non-active interior sensor.

0074A third shape “C” includes a shape in the form of an “L”. Analysis of this shape, however, reveals that there are not interior sensors. That is, for each of the interior sensor candidates, there is an angular separation (as shown by the arrows <b>820</b>) that is greater than 135 degrees.

0075However, the “L” shaped group could be broken down to eliminate sensors for other reasons.

0076<figref idref="DRAWINGS">FIG. 9</figref> shows a group of sensors being tracked over multiple frames, according to an embodiment. For an embodiment, sensors of a group are not allowed to be greater than a predetermined distance from each other. For example, the group of time interval T<b>2</b> of <figref idref="DRAWINGS">FIG. 9</figref> includes sensors Sx, Sy, Sz that can be determined to be physically too far in distance from the sensor Sa. Therefore, the sensors Sx, Sy, Sz can be eliminated from group of the time interval T<b>2</b>. For an embodiment, the sensors Sx, Sy, Sz can be the basis for the formation of a new group.

0077<figref idref="DRAWINGS">FIG. 10</figref> shows an initially identified group of sensors being split, according to an embodiment. As previously stated, the sensors Sx, Sy, Sz can be the basis for the formation of a new group, Group<b>2</b>. <figref idref="DRAWINGS">FIG. 10</figref> shows subsequent groups of following time intervals T<b>1</b>, T<b>2</b>, T<b>3</b>, T<b>4</b>, T<b>5</b>, T<b>6</b>. As show, the group of the time interval T<b>0</b> may provide the basis for more than one group. For an embodiment, the initial group is split, and separate groups are tracked over the subsequent time intervals.

0078<figref idref="DRAWINGS">FIG. 11</figref> shows an initially identified group of sensors being split, and two separate trails being formed, according to an embodiment. As shown, a group is initially formed at time interval T<b>0</b>. Further, the group is tracked to time interval T<b>1</b>. However, at time interval T<b>2</b>, the group is split into two groups because retaining the active sensor as one group would be, for example, too large. At time intervals T<b>3</b>, T<b>4</b>, T<b>5</b>, the existing group and the newly formed group create two separate trails (trail <b>1</b>, trail <b>2</b>).

0079<figref idref="DRAWINGS">FIG. 12</figref> shows an obstruction that includes a doorway, and formation of the group, according to an embodiment. This doorway and wall obstruction is similar to one depicted in <figref idref="DRAWINGS">FIG. 5</figref>. However, the potential group formed by the activated or triggered sensors S<b>1</b>-S<b>11</b> that sense motion greater than the motion threshold, encompasses the wall <b>1210</b>, and passes through the doorway <b>1220</b>. However, as previously mentioned, at least some embodiment include a distance threshold in which activated sensors that are initially identified as member cannot be retained as members if the distance between them exceeds the distance threshold. In relation to the initially identified group of sensors S<b>1</b>-S<b>11</b>, and embodiment includes the distance between sensors being measured around and obstacles, such as the wall <b>1210</b>. Therefore, the distance is measured as depicted by the distance arrow. That is, the distance extends from S<b>1</b> to S<b>11</b> through the doorway <b>1220</b>. In order for the initially identified group to be retained, this distance must be less than the distance threshold.

0080<figref idref="DRAWINGS">FIG. 13</figref> is a flow chart that includes steps of a method of tracking motion, according to another embodiment. A first step <b>1310</b> includes identifying a group of sensors that includes a plurality of neighboring sensors sensing motion greater than a motion threshold during a time interval. A second step <b>1320</b> includes tracking motion, comprising linking the group to at least one past group of at least one past time interval.

0081While the describe embodiments include tracking motion of identified groups across multiple time intervals, it is to be understood that for at least some embodiments a lack of motion is tracked. This is useful, for example, for tracking movement of an occupant who may have stopped moving.

0082For an embodiment, the tracked motion of the group includes at least one of the plurality of neighboring sensors within the group being different than a plurality of sensors of the at least one past group. That is, if for example, an occupant is moving within an area or a structure, the group of sensors that detect motion of the occupant changes from one time interval to a following time interval. At least some embodiments include confirming that the group changes are large enough to constitute motion of the group.

0083For at least some embodiments, the group of sensors is a subset of a larger set of sensors, and location data of each of the larger set of sensors is obtained, thereby allowing a determination of which of the larger set of sensors are neighboring sensors. That is, the group of sensors belongs or is within a larger set of sensors that are spaced about an area, room or structure. For at least some embodiments, the tracking of motion needs knowledge of what sensors are neighboring sensors. Information about the location of each of the sensors of the larger set of sensors allows a determination of which sensor are neighboring sensors. For example, the set of sensor closest to a sensor can be determined to be neighboring sensors. The set of sensors that are within a threshold of physical distance from a sensor can be determined to be neighboring sensors.

0084At least some embodiments include obtaining location data of obstructions located within the larger set of sensors. This information can be useful for group determination. For example, sensors may be identified as neighboring sensors based on their location. However, if the sensor are on opposite sides of an obstruction (such as, a wall) then it can be determined that the neighboring sensors cannot be included within a common group. Further, if a large group of sensors is formed that includes sensors in a doorway and sensors on both sides of walls, sensors far away from the doorway should not be included within the grouping. Further, if tracked motion appears to travel straight through an obstruction, a path of the tracked motion can be altered to pass around the obstruction.

0085For at least some embodiments, identifying the group of sensors that are included within the plurality of neighboring sensors sensing motion greater than the threshold during a time interval includes identifying a sensor that senses motion greater than the threshold, and then searching identified neighboring sensors of the sensor to find sensors that also sense motion greater than the motion threshold during the time interval. For at least some other embodiments, identifying the group of sensors that are included within the plurality of neighboring sensors sensing motion greater than the threshold during a time interval includes identifying a sensor that senses motion greater than the threshold, and then searching identified neighboring sensors of the sensor that also sense motion greater than a second motion threshold during the time interval. For an embodiment, the second motion threshold is less than the first motion threshold.

0086At least some embodiments further include refining the group of sensors, including checking locations of each of the plurality of sensors of the group and comparing the locations within locations of known obstructions, and eliminating sensor of the group that cannot be included within the group due to the location of the obstruction. That is, once a group of sensor is identified, the group is further analyzed to make sure it makes physical sense. That is, the group does not include sensors which clearly cannot be in a common group because an obstruction is located between sensors of the group.

0087At least some embodiments further include refining the group of sensors by determining whether the group is too large. That is, an initially determined group may be too large to actually make such a grouping possible. A threshold number of sensors can be selected in which a grouping is limited to be less than. For an embodiment, the refining of the group includes eliminating the existence of sensors of a group that are located a physical distance of greater than a threshold. That is, a physical distance can be selected or identified, wherein two sensors cannot be within a common group if the physical distance between them exceeds the selected or identified distance threshold.

0088For at least some embodiments, if the group is determined to be too large, then the group is split into multiple groups or one or more sensors are eliminated from the group. For an embodiment, if the group is split, then a determination is made of how many groups the sensors are split into (that is, 0, 3, 2, . . . ) where the groups formed (taking into account recent movement of groups), and which groups certain sensor are to belong.

0089At least some embodiments further include determining whether the group has a proper shape, comprising analyzing the plurality of sensors of the group to determine that locations of each of the sensors of the plurality of sensors indicates maintaining the sensor within the group. For an embodiment, determining whether the group has a proper shape includes identifying whether the group includes inactive interior sensors.

0090For at least some embodiment, tracking motion of the group includes matching the group of a current time interval with groups of prior time intervals to determine whether a trail of motion of the group is formed over a plurality of the time intervals.

0091At least some embodiments include confirming that the group originated from the at least one past group of at least one past time interval, including confirming that the group is location within a common area as the at least one past group, confirming that the group includes at least one neighboring sensors of the at least one past group. At least some embodiments include confirming that the group originated from the at least one past group of at least one past time interval, comprising confirming that the group is location within a common area as the at least one past group, confirming that the group includes at least one neighboring sensors that is a neighboring sensor of at least one sensor of the at least one past group.

0092At least some embodiments include designating the group as a child of the at least one prior group upon confirmation of origination. At least some embodiments include evaluating all child groups of a prior group to determine which of the child groups provides a most likely trail of the prior group. At least some embodiments include ranking each of the child groups based on at least one of a number of shared sensors, a quantity of sensors within the child group, a direction of travel between the prior group and the child group, and selecting the child group with the highest rank as the most likely trail of the prior group. Further, groups that include back and forth motion (jitter) may not be tracked.

0093Although specific embodiments have been described and illustrated, the described embodiments are not to be limited to the specific forms or arrangements of parts so described and illustrated. The embodiments are limited only by the appended claims.