Abstract:
User self-installation of a sensor network for activity monitoring may be facilitated by providing a computer system that prompts the user through the installation process. Particularly, the computer system may prompt the user to identify an object to which a sensor has been attached and the activities with which identified objects are associated. The computer may prompt with potential activities based on the object identified by the user. The elicited information may be used to automatically generate a model, which may be automatically improved over time by examining the history of sensor readings. Thereafter, based on the data produced by the sensors, the system identifies what activities are actually being completed.

Description:
BACKGROUND 
       [0001]    This relates generally to the use of sensor networks. 
         [0002]    A sensor network is a collection of sensors that may be distributed throughout a facility in order to determine information about activities going on within that facility. Examples of sensor network applications include in-home, long-term health care, in-home care for elderly, home or corporate security, activity monitoring, and industrial engineering to improve efficiency in plants, to mention a few examples. 
         [0003]    In many cases, the installation of the array is done by a technician who is experienced and knowledgeable about how to install such an array. However, in many applications, including in-home applications for example, the need for a technician to install and maintain the array greatly increases the cost. Thus, it is desirable to provide a sensor network that may be self-installed by a user or a user&#39;s family member or caretaker. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0004]      FIG. 1  is a perspective view of one embodiment of the present invention; 
           [0005]      FIG. 2  is a schematic depiction of one embodiment of the present invention; 
           [0006]      FIG. 3  is a flow chart for one embodiment of the present invention; 
           [0007]      FIG. 4   a  is an object entry user interface for one embodiment; and 
           [0008]      FIG. 4   b  is an activity entry user interface for one embodiment. 
       
    
    
     DETAILED DESCRIPTION 
       [0009]    In some embodiments, user self-installation of a sensor network can be improved or facilitated by asking the user to specify the activity monitored by the sensor. To facilitate this practice, the user may be provided with an electronic device that allows the user to associate sensors with objects or states, and displays user selectable activity options and/or allows the user to enter their own options. Using this elicited information, the device may automatically build a model, monitor the sensor data it receives over time and identify what activities are being undertaken. 
         [0010]    As a simple example, the user may indicate that a shake sensor was placed on a refrigerator door and that the activities related to the refrigerator door might be getting a drink, preparing a meal, filling the refrigerator with groceries, getting ice, or determining whether additional groceries may be needed. Thus, when the refrigerator door sensor fires, the system has a variety of options to consider when identifying why the user was opening the refrigerator. However, using a sensor network, the system can obtain additional information from which it may be able to probabilistically identify the actual activity. For example, if, within a certain time, the user opened another drawer that includes silverware and, still another cabinet that includes plates, the probability may be higher that the user is preparing a meal. 
         [0011]    Feedback may be obtained to determine whether or not this determination is correct. Based on the feedback received and/or on automated machine learning algorithms, the machine may improve its internal model of sensors, objects, states, and activities. A state relates to an object and defines its current condition (e.g. on, off, open, closed, operating, not operating, etc.). 
         [0012]    Thus, referring to  FIG. 1 , a home installation is illustrated. It is applicable to home health care, care for the elderly, or home monitoring. However, the present invention is not limited to these applications. 
         [0013]    Thus,  FIG. 1  shows a user&#39;s kitchen, including a refrigerator  12 , a counter  14 , a sink  16 , a faucet  15 , and a sensor  18  on the counter front. The sensor  18  may be a proximity sensor. Typically sensors for sensor networks are wireless and battery powered. A drawer  20  may include a handle  22  with a touch sensor  24 . The refrigerator  12  may include a handle  26  with a touch sensor  28 . A camera  30  may provide information about what is actually happening. Thus, the information from the camera  30  may provide feedback, which may be utilized by the machine to learn what activities correspond to received sensor signals and signal timing. 
         [0014]    Referring to  FIG. 2 , the sensor network, in accordance with one embodiment, may include a large number of sensors  32 , logically coupled to a computer  34 . The computer  34  may include a wireless transceiver  38  and a controller  36 . The camera  30  may be directly connected or wirelessly connected to the computer  34 . The controller  36  may include storage that stores software and/or gathered sensor data. A network interface  42  may enable the computer  34  to interface wirelessly over the Internet or over a cellular network with a remote operations center. A user interface  40  provides the user with a device to enter selections or view system status and output, such as a touch screen display. A radio frequency identifier (RFID) reader or receiver  41  and memory  43  may also be coupled to the computer. 
         [0015]    Referring to  FIG. 3 , a configuration sequence  47  may be followed by model generation  45  and then an execution sequence  44 . In the configuration sequence  47 , a new sensor is configured and, in the execution sequence  44 , the sensor is actually used to collect information about activities being done by the user. The configuration sequence  47  is repeated for each added sensor. 
         [0016]    Thus, in the initial configuration sequence  47  for each sensor, the user causes the selected physical sensor to interact with the system, as indicated in block  46 . The system then detects the sensor  32  at  52 . This may be done by reading an RFID tag on the sensor using the RFID reader  41  so that the sensor  32  is identified. Other identification methods may include, but are not limited by, using infrared wireless communication, pushing buttons on the sensor  32  and the user interface  40  simultaneously, pushing a button on the user interface  40  while shaking the sensor  32 , having a bar code reader on the user interface  40  to read a 1D or 2D code on the sensor  32 , or using keyboard entry via computer  34  or user interface  40  of a sensor identifier number. For example, the sensor may have a bar code that identifies the type of sensor (e.g. motion, touch proximity, etc) and its identifier. 
         [0017]    Then, an object selection system may be implemented in block  54 . The user may select or identify what object the sensor is attached to in block  48  using a user interface  40  that may be the interface shown in  FIG. 4   a  in one embodiment. The sensors may be adapted for easy installation, for example, using an adhesive strip with a peel off cover. The selection may be entered on the user interface, for example, via a touch screen. 
         [0018]    The user interface  40  may provide a list of objects within the home to select from, for example, by selecting the corresponding picture on a touch screen. As another example, the user can select the first letter of the object at A to get a display of objects in window B starting with that letter as indicated in  FIG. 4   a . The user may also enter new objects to be added to any current list. Then the object sensor pair is added to the set representing the sensor network, as indicated by block  56 . 
         [0019]    The user may also select the activities the sensor is intended to be associated with in block  50 . The activity selection system  58  is used for this purpose. Each object may be associated with multiple activities in block  60 . In one embodiment, shown in  FIG. 4   b , the user interface may be a mouse selectable drop down menu that includes activities (e.g. meal preparation, ordering take out, etc.) potentially applicable to the previously identified object, while still allowing the user to identify a new or existing activity not yet in the list (i.e. “enter a new activity”). In the example shown in  FIGS. 4   a  and  4   b , the user identified the object to which the sensor was attached as a kitchen drawer. At this point, the flow is iterated for each sensor identified by the user, either configuring or reconfiguring each sensor, each initiated through block  46 . 
         [0020]    In block  62 , a model generation system generates a model  64  of the relationships between activities and objects, as provided by the user, and as learned by the system thereafter. 
         [0021]    During the execution  44 , each sensor sends data  70  to the observation manager  68  in computer  34  via transceiver  38  in one embodiment. The observation manager  68  collects sensor information and any other feedback, such as camera or user interface feedback as inputs. Based on this information and the model  64 , the execution engine  66  determines what activity was being done as indicated in block  74 . This determination may then be used in a model learning module  89  to improve the model  64  based on experience. 
         [0022]    Model optimization using machine learning techniques may be implemented in software, hardware, or firmware, as indicated in  FIG. 3 . In software embodiments, the software may be implemented by instructions stored on a computer readable medium such as a semiconductor, optical or magnetic memory, such as memory  43 . The instructions may be executed by the controller  36 . The model optimization operation begins at  62 , where user inputs are synthesized into a model. Over time, sensor data is collected by the observation manager  68 . The data and the activity determined by the data are analyzed by the model learning block  89 . Ground truth may also be considered, gathered by video analysis of the camera data or by asking the user via the user interface at key intervals to verify the activity he or she is doing. The model  64  may then be updated appropriately. 
         [0023]    For example, the activity of operating the faucet (detected by proximity sensor  18 ), followed by the activity of opening the refrigerator door (as sensed by touch sensor  28 ), followed by the activity of pulling a dish out of the cabinet (detected by sensor  24 ), all within a certain window of time could indicate the activity of food preparation, rather than the task of preparing a grocery shopping list. At periodic intervals, camera information or user inquiries may be used to refine the model of how the sensors, objects, states, and activities relate. For example, the user can be asked to indicate what task the user just did, via the user interface. Thus, the computer can then reinforce over time that, given a sensor dataset with given time, a certain activity is more probable. In this way, the system can identify what activities the user is doing, in many cases without the need for technician installation. 
         [0024]    References throughout this specification to “one embodiment” or “an embodiment” mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one implementation encompassed within the present invention. Thus, appearances of the phrase “one embodiment” or “in an embodiment” are not necessarily referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be instituted in other suitable forms other than the particular embodiment illustrated and all such forms may be encompassed within the claims of the present application. 
         [0025]    While the present invention has been described with respect to a limited number of embodiments, those skilled in the art will appreciate numerous modifications and variations therefrom. It is intended that the appended claims cover all such modifications and variations as fall within the true spirit and scope of this present invention.