Lift Me Up - CS4222 Group 9

Elderly Falls – How big is the problem?  About one third of the elder population over the age of 65 falls each year, and the risk of falls increases proportionately with age. At 80 years, over half of seniors fall annually.  NUH said that about 11 per cent of its daily admissions of patients above 65 are for falls.  Up to 70 per cent of elderly people who fall develop a fear of it happening again. This fear could make them hesitant to leave the house or take part in various activities. It could lead to further weakness, social isolation and even depression.  This will become increasingly important as the population ages. By 2030, one-fifth of Singaporeans will be 65 and above.

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How expensive can these devices be? US $ / Unit

Our solution – Lift Me Up  A free application run on Android platform with min API level 11  Smart fall detection system to prevent false alarm  Multiple communication channels : SMS, DTN communication  Employs the location service provided in by the smart device

Lift Me Up  Fall Detection  Sensors  Algorithm  Emergency Message  Communication channels  Location  Alarm

Fall Detection  Accelerometer  TYPE_ACCELEROMETER TYPE_ACCELEROMETER  50 samples/second  3 states confirmation  Primary fall (pattern)  Long lie (time threshold)  Confirmation  Sends emergency message after confirmation

Emergency Message  Communication Channels  SMS  Mobile Network (GCM Push)  DTN  Location  Android Location Services API  Location Provider (Network Provider + GPS)  Geofencing  Sound and visual alarms  Alerts people nearby  Provides contact information

Fall Detection Algorithm

What Is a Fall?  A fall is a sudden change of body position (from a height) coming to rest on the ground (after a hard impact), followed by a moment on inactivity (subject becomes unconscious).  Focus is on serious fall – subject is immobile & unable to get help.

What Is A Fall for Accelerometer?

Acceleration Pattern

Data Processing

Primary Fall

start Detecting Primary Fall No Yes

Long Lie State  After Primary Fall detected:  Long Lie State: Check if user become unconscious.  Acceleration : nearly 1G.  If detect any further movement, recover from Primary Fall.

Walking VS. Standing

start Detecting Primary Fall Long Lie yes No Recov er

Confirm Fall  After Long Lie State: Then we can confirm a Serious Fall is detected.  Start a timer. Before time out, user can cancel alarm and stop timer.  After time out, message will be sent for help.

start Detecting Primary Fall Long Lie Time Out yes No Recover Recover/ Cancel Send Messag e yes

Challenges  False Positives:  Timer to cancel alarm  Add extra constraint to detect primary fall  such as orientation changed  Users carry phones differently  Cannot be used in all situations

Conclusion  Low-cost alternative for people who cannot afford dedicated medical alert solution.  Very effective if emergency is correctly triggered

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