Generating and Displaying Metrics of Interest Based on Motion Data

Abstract

In a general aspect, metrics of interest are generated based on motion data and displayed. In some aspects, a method includes obtaining channel information based on wireless signals communicated through a space over a time period by a wireless communication network. The space includes a plurality of locations. The method includes generating motion data based on the channel information. The motion data includes motion indicator values and motion localization values for the plurality of locations. The method further includes identifying, based on the motion data, an actual value for a metric of interest for the time period; identifying, based on user input data, a benchmark value for the metric of interest for the time period; and providing, for display on a user interface of a user device, the actual value for the metric of interest and the benchmark value for the metric of interest.

1 . A method comprising:
 generating channel information based on radio-frequency wireless signals communicated between one or more pairs of wireless communication devices according to a wireless communication protocol of a wireless communication network, wherein the radio-frequency wireless signals are communicated through a space over a time period, and the channel information represents the space traversed by the radio-frequency wireless signals;   generating motion data, by operation of a motion detection engine, based on the channel information, the motion data comprising a series of vectors comprising a vector m t   =[m t L t,1  m t L t,2  . . . m t L t,N ] for each respective time point (t) in a series of time points within the time period,
 wherein m t  represents motion indicator values indicative of a degree of motion that occurred in the space for each time point (t) in the series of time points within the time period; and 
 L t,N  represents motion localization values for the plurality of locations in the space, the motion localization value for each individual location representing a relative degree of motion detected at the individual location (N) for each time point in the series of time points within the time period; 
   by operation of a pattern extraction engine, processing the series of vectors to generate activity data for the time period, wherein the activity data comprises an actual value for a metric of interest for the time period, and processing the series of vectors comprises:
 determining an aggregate degree of motion that occurred at each of the individual locations during the time period; 
 determining a duration of activity that occurred at each of the individual locations during the time period; and 
 determining a duration of inactivity that occurred at each of the individual locations during the time period; 
   identifying, based on user input data, a benchmark value for the metric of interest for the time period; and   providing, for display on a user interface of a user device, the actual value for the metric of interest and the benchmark value for the metric of interest.   
     
     
         2 . The method of  claim 1 , wherein the user input data comprises:
 a first time interval within the time period, the first time interval indicative of a time interval during which a person expects to be asleep; and   a targeted duration of sleep during the first time interval.   
     
     
         3 . The method of  claim 2 , wherein the actual value of the metric of interest comprises at least one of:
 a total duration of sleep observed during the first time interval;   a total duration of movement observed during the first time interval;   a degree of motion observed for each time point within the first time interval; or   sleep levels observed during the first time interval.   
     
     
         4 . The method of  claim 3 , wherein the sleep levels observed during the first time interval comprises:
 durations of restful sleep within the first time interval;   durations of light sleep within the first time interval; and   durations of disrupted sleep within the first time interval.   
     
     
         5 . The method of  claim 1 , wherein the user input data comprises:
 a second time interval within the time period, the second time interval indicative of times during which a person expects to be awake; and   a targeted duration of movement during the second time interval.   
     
     
         6 . The method of  claim 5 , wherein the actual value of the metric of interest comprises at least one of:
 a total duration of movement observed during the second time interval;   a degree of motion observed at each location for each time point within the second time interval; or   the location exhibiting the highest degree of motion during the second time interval.   
     
     
         7 . The method of  claim 1 , wherein the user input data comprises an indication of a time duration within the time period during which motion is not expected, and the method further comprises:
 determining, based on the user input data and the motion data, that motion has occurred during the time duration; and   providing, for display on the user interface of the user device, a notification that motion has occurred within the time du tunable-frequency ration during which motion is not expected.   
     
     
         8 . The method of  claim 1 , wherein the user input data comprises an indication of one or more locations at which motion is not expected, and the method further comprises:
 determining, based on the user input data and the motion data, that motion has occurred at the one or more locations; and   providing, for display on the user interface of the user device, a notification that motion has occurred at one or more of the locations at which motion is not expected.   
     
     
         9 . The method of  claim 1 , wherein each wireless communication device is located in a respective location of the plurality of locations. 
     
     
         10 . The method of  claim 1 , wherein the radio-frequency wireless signals communicated through the space comprises radio-frequency wireless signals exchanged on wireless communication links in the wireless communication network, and each motion indicator value represents the degree of motion detected from the radio-frequency wireless signals exchanged on a respective one of the wireless communication links. 
     
     
         11 . A non-transitory computer-readable medium in a wireless communication device of a wireless communication network comprising instructions that are operable, when executed by data processing apparatus of the wireless communication device, to perform operations comprising:
 generating channel information, wherein the channel information is generated based on radio-frequency wireless signals communicated between the wireless communication device and one or more other wireless communication devices according to a wireless communication protocol of the wireless communication network, wherein the radio-frequency wireless signals are communicated through a space over a time period, and the channel information represents the space traversed by the radio-frequency wireless signals;   generating, by operation of a motion detection engine, motion data based on the channel information, the motion data comprising a series of vectors comprising a vector m t   =[m t L t,1  m t K t,2  . . . m t L t,N ] for each respective time point (t) in a series of time points within the time period:
 wherein m t  represents motion indicator values indicative of a degree of motion that occurred in the space for each time point (t) in the series of time points within the time period; and 
 L t,N  represents motion localization values for the plurality of locations, the motion localization value for each individual location representing a relative degree of motion detected at the individual location (N) for each time point in the series of time points within the time period; 
   processing, by operation of a pattern extraction engine, the series of vectors to generate activity data for the time period, wherein the activity data comprises an actual value for a metric of interest for the time period, and processing the series of vectors comprises:
 determining an aggregate degree of motion that occurred at each of the individual locations during the time period; 
 determining a duration of activity that occurred at each of the individual locations during the time period; and 
 determining a duration of inactivity that occurred at each of the individual locations during the time period; 
   identifying, based on user input data, a benchmark value for the metric of interest for the time period; and   providing, for display on a user interface of a user device, the actual value for the metric of interest and the benchmark value for the metric of interest.   
     
     
         12 . The non-transitory computer-readable medium of  claim 11 , wherein the user input data comprises:
 a first time interval within the time period, the first time interval indicative of a time interval during which a person expects to be asleep; and   a targeted duration of sleep during the first time interval.   
     
     
         13 . The non-transitory computer-readable medium of  claim 12 , wherein the actual value of the metric of interest comprises at least one of:
 a total duration of sleep observed during the first time interval;   a total duration of movement observed during the first time interval;   a degree of motion observed for each time point within the first time interval; or   sleep levels observed during the first time interval.   
     
     
         14 . The non-transitory computer-readable medium of  claim 13 , wherein the sleep levels observed during the first time interval comprises:
 durations of restful sleep within the first time interval;   durations of light sleep within the first time interval; and   durations of disrupted sleep within the first time interval.   
     
     
         15 . The non-transitory computer-readable medium of  claim 11 , wherein the user input data comprises:
 a second time interval within the time period, the second time interval indicative of times during which a person expects to be awake; and   a targeted duration of movement during the second time interval.   
     
     
         16 . The non-transitory computer-readable medium of  claim 15 , wherein the actual value of the metric of interest comprises at least one of:
 a total duration of movement observed during the second time interval;   a degree of motion observed at each location for each time point within the second time interval; or   the location exhibiting the highest degree of motion during the second time interval.   
     
     
         17 . A system, comprising:
 a plurality of wireless communication devices in a wireless communication network, the plurality of wireless communication devices configured to transmit radio-frequency wireless signals through a space;   a computer device comprising one or more processors configured to perform operations comprising:
 generating channel information, wherein the channel information is generated based on the radio-frequency wireless signals communicated between one or more pairs of the plurality of wireless communication devices according to a wireless communication protocol of the wireless communication network, and the channel information represents the space traversed by the radio-frequency wireless signals over a time period; 
 generating, by operation of a motion detection engine, motion data based on the channel information, the motion data comprising a series of vectors comprising a vector m t   =[m t L t,1  m t L t,2  . . . m t L t,N ] for each respective time point (t) in a series of time points within the time period.
 wherein m t  represents motion indicator values indicative of a degree of motion that occurred in the space for each time point (t) in the series of time points within the time period; and 
 L t,N  represents motion localization values for the plurality of locations, the motion localization value for each individual location representing a relative degree of motion detected at the individual location (N) for each time point in the series of time points within the time period; 
 
 processing, by operation of a pattern extraction engine, the series of vectors to generate activity data for the time period, wherein the activity data comprises an actual value for a metric of interest for the time period, and processing the series of vectors comprises:
 determining an aggregate degree of motion that occurred at each of the individual locations during the time period; 
 determining a duration of activity that occurred at each of the individual locations during the time period; and 
 determining a duration of inactivity that occurred at each of the individual locations during the time period; 
 
 identifying, based on user input data, a benchmark value for the metric of interest for the time period; and 
 providing, for display on a user interface of a user device, the actual value for the metric of interest and the benchmark value for the metric of interest. 
   
     
     
         18 . The system of  claim 17 , wherein the user input data comprises:
 a first time interval within the time period, the first time interval indicative of a time interval during which a person expects to be asleep; and   a targeted duration of sleep during the first time interval.   
     
     
         19 . The system of  claim 18 , wherein the actual value of the metric of interest comprises at least one of:
 a total duration of sleep observed during the first time interval;   a total duration of movement observed during the first time interval;   a degree of motion observed for each time point within the first time interval; or   sleep levels observed during the first time interval.   
     
     
         20 . The system of  claim 19 , wherein the sleep levels observed during the first time interval comprises:
 durations of restful sleep within the first time interval;   durations of light sleep within the first time interval; and   durations of disrupted sleep within the first time interval.   
     
     
         21 . The system of  claim 17 , wherein the user input data comprises:
 a second time interval within the time period, the second time interval indicative of times during which a person expects to be awake; and   a targeted duration of movement during the second time interval.   
     
     
         22 . The system of  claim 21 , wherein the actual value of the metric of interest comprises at least one of:
 a total duration of movement observed during the second time interval;   a degree of motion observed at each location for each time point within the second time interval; or   the location exhibiting the highest degree of motion during the second time interval.   
     
     
         23 . A method, comprising:
 receiving an actual value for a metric of interest for a time period, wherein:
 the actual value for the metric of interest for the time period is included in activity data for the time period, wherein the activity data is determined by processing a series of vectors in motion data identified based on motion data; 
 the motion data is generated based on channel information, wherein the channel information represents a space traversed by radio-frequency wireless signals over a time period; 
 the channel information is generated-based on the radio-frequency wireless signals communicated between respective pairs of the wireless communication devices according to a wireless communication protocol of a wireless communication network through the space, the space comprising a plurality of locations; and 
 the series of vectors comprise a vector m t   =[m t L t,1  m t L t,2  . . . m t L t,N ] for each respective time point (t) in a series of time points within the time period:
 wherein m t  represents motion indicator values indicative of a degree of motion that occurred in the space for each time point (t) in the series of time points within the time period; and 
 L t,N  represents motion localization values for the plurality of locations, the motion localization value for each individual location representing a relative degree of motion detected at the individual location (N) for each time point in the series of time points within the time period; 
 
   receiving a benchmark value for the metric of interest for the time period, wherein the benchmark value for the metric of interest is identified based on user input data; and   displaying, on a user interface of a user device, the actual value for the metric of interest relative to the benchmark value for the metric of interest,   wherein processing the series of vectors in the motion data comprises:
 determining an aggregate degree of motion that occurred at each of the individual locations during the time period: 
 determining a duration of activity that occurred at each of the individual locations during the time period: and 
 determining a duration of inactivity that occurred at each of the individual locations during the time period 
   
     
     
         24 . The method of  claim 23 , further comprising generating a notification in response to the actual value for the metric of interest being greater than or equal to the benchmark value for the metric of interest. 
     
     
         25 . The method of  claim 23 , wherein each wireless communication device is located in a respective location of the plurality of locations. 
     
     
         26 . The method of  claim 23 , wherein the radio-frequency wireless signals communicated through the space comprises radio-frequency wireless signals exchanged on wireless communication links in the wireless communication network, and each motion indicator value represents the degree of motion detected from the radio-frequency wireless signals exchanged on a respective one of the wireless communication links. 
     
     
         27 - 30 . (canceled)