Project x1.1 Presentation
The current US population of those 65 years and older is over 40 million today, and the federal Census Bureau predicts that will more than double, to nearly 87 million, by midcentury. There are many individuals who are concerned about and/or are trying to remain in their homes, a class that is being called Aging in Place. This product idea grew out of discussions with many people about the concern of their parents or relatives growing older and insisting on living alone in the family home – the Aging in Place population. One of the major concerns in this situation is that an individual has a medical episode, a fall or some other physical problem that will not be discovered for some period of time, which would turn the episode from a minor incident into a major problem. This is where the idea for ‘eCompanion’ was born. With the currently available technology in sensors for movement / position recognition, wireless interconnectivity and mobile devices, a solution is possible where an individual can be monitored and anomalies detected that can trigger contacting a monitoring service without the need of either a 24 visitation service or pushing a button. With 3axis accelerators monitored in a worn band, a database of the individual's typical movements will be used to provide input for lapses in movement or movements out of normal pattern. A database with adaptive learning capability will be used to input and to determine the typical movement patterns of the monitored individual and convert this into useful information about their daily activities. When the system detects a break in the normal pattern it can automatically trigger an alert sequence.
![Architecture
Major Components:
• Sensing band worn by MI
• Tethered smartphone
• Cloud based data storage /
learning algorithm
• Call support center
• Resource individual [or Caregiver]
• 911 service as backup
User
database
911 Service
Data
Data
Tethered
Smartphone
Sensor Band
Learning
algorithm
Call
Support
Resource
Individual](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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Project x1.1 Presentation
- 2. Architecture Major Components: • Sensing band worn by MI • Tethered smartphone • Cloud based data storage / learning algorithm • Call support center • Resource individual [or Caregiver] • 911 service as backup User database 911 Service Data Data Tethered Smartphone Sensor Band Learning algorithm Call Support Resource Individual
- 3. Sensing Band Optical heart rate sensor 3-axis accelerometer Gyrometer Ambient light sensor UV sensor Skin temperature sensor Capacitive sensor Galvanic skin response Microphone Haptic vibration motor Bluetooth LE (Low Energy) Other possible options: Currently using Microsoft Health Band:
- 4. Tethered Smartphone Used to push / pull data from sensor band to / from cloud based algorithm system. Nominal Requirements: • Platform: Windows / Android • Processor: Qualcomm Snapdragon • GPS: A-GPS & GLONASS • WiFi: Wi-Fi 802.11 b/g/n • 3G cellular connectivity
- 5. Fall Detection by Vector Analysis MI fall is detected by continually reading the band’s 3-axis accelerometer and gyrometer to provide a six axis look at out of normal movement patterns. Since no absolute reference exists, variations between incremental readings are compared on timed intervals for excessive movement that indicates a fall pattern. Time Accel X (g) Accel Y (g) Accel Z (g) Gyro X (rad/sec) Gyro Y (rad/sec) Gyro Z (rad/sec) 0.921738808 -0.008774 -0.969223 -0.242905 -0.034438 0 0 0.921739109 -0.012314 -0.971085 -0.236801 -0.034946 0 0 0.921739340 -0.014008 -0.970306 -0.234909 -0.03497 0 0 0.921739711 -0.010468 -0.969437 -0.234192 -0.050642 0 0 0.921739896 -0.011032 -0.969849 -0.252548 -0.048254 0 0 0.921740081 -0.012665 -0.968796 -0.261963 -0.038064 0 0 0.921740313 -0.013107 -0.966553 -0.27182 -0.03409 0 0 0.921740486 -0.013107 -0.966553 -0.27182 -0.031265 0 0 0.921740741 -0.010574 -0.967148 -0.261002 -0.032383 0 0 0.921740926 -0.012222 -0.969315 -0.252792 -0.043219 0 0 0.921741100 -0.009903 -0.969162 -0.251129 -0.024979 0 0 0.921741424 -0.011353 -0.969131 -0.243607 -0.026266 0 0 0.921741667 -0.014862 -0.970001 -0.246445 -0.034849 0 0 0.921741840 -0.009201 -0.967194 -0.256058 -0.032421 0 0 0.921742072 -0.010162 -0.968918 -0.247589 -0.03133 0 0 Readings are compared real time looking for patterns that fit learning algorithm developed that model individual’s projected fall sequence.
- 6. Automatic Algorithm Response Basic response function based on analyzing vector movements of band against machine learned algorithm adjusted against specific MI movement patterns Band event detected Band being worn [HR] db indicates escalation Band dropped warning No No Yes Robo Alert to Band Band Alert Timeout Robo Call to phone MI responded Escalate to Call center Yes Event Counter No response Local reset No response MI OK
- 7. Call Support Response Sequence based on Caregiver’s instructions on time of day contact allowances and strength of algorithm’s response, before direct contact to MI and hand of to 911 Notification from Algorithm Check band data feed Time allows CareGiver contact Low priority Alert band / call MI Questionable Yes Solid Call CareGiver CareGiver Responce Call MI MI responded Escalate to 911 No Timer for escalation of response No response Responded No response MI OKReset / Store sequence Timeout