Bor-rong Chen, PhDJoseph Gwin, PhD

BioSensics LLC

Automatic Fall Detection and Risk of Falling Assessment with Wearable Sensors

Automatic Fall Detection and Risk of Falling Assessment with Wearable Sensors

OOODetected Falls

Bijan Najafi, PhDUniversity of Arizona

2

Elderly Population:• 2006: 37 million US residents were age 65 and above (over 12.4% of the total population).• 2030: 71.5 million in 2030, representing nearly 20% of the total US population.• Today: more than 10,000 people turn 65 every day for the coming 20 years.

Falls:• One in three adults age 65 and older falls each year. 1

Lifesaving Benefits of Automatic Fall Detection:• Three-minute reduction in call-to-shock time improves odds of survival almost four-fold.5

Cost Savings of Fall Prevention:• Hospitalization cost for a fall injury is $17,500 2

• Resulting injuries may require admission to long-term care facility at $87,235/year 3,41. Hausdorff JM, et al. Archives of Physical Medicine and Rehabilitation 2001;82(8):1050–62. Roudsari BS, et al. Int J Care Injured 2005;36:1316-22.3. Metlife Market Survey of Long-Term Care Costs (2011)4. Stevens JA. Falls among older adults—risk factors and prevention strategies. National Council on the Aging; 2005. 5. Davis, Robert. The price of just a few seconds lost: People die. USA Today (May, 2005)

Background

• MEMS inertial measurement unit (IMU)– 3D Accelerometer, Gyroscope, Magnetometer

• Low power personal area wireless technology– e.g. Bluetooth, IEEE 802.15.4 (Zigbee)

Wearable Sensors

Physical Activity Monitoring

Raw Acceleration Data

Physical Activity – Detailed Analysis

– Postural Transitions– Walking Characterization– Lying Characterization

Postural Sway

Gait

• Commonly Employed Approaches– Self-report (wireless help button)– Simple threshold based detection

Fall Detection

Fall Detection

• Challenge:– Distinguish real falls

from normal movements– avoid false alarms Raw Acceleration Data

Simple thresholds for instantaneous acceleration amplitude suffer from either too many false alarms or miss the falls

PAMSysTM

Postural Analysis:

Sitting, Standing, Lying, Walking

Posture-dependent False Alarm Removal

Rules

Post Shock Analysis

Peak Detection(shock detected?)

Shock Threshold

Transverse Plane

Acceleration

3-axis Acceleration

aF, aL, aVReport

Falls

Posture-informed Fall Detection

11

Fall Detection Algorithm

Yes

No

Preliminary Results

• Clinical study 1 – Simulated falls– 10 adult subjects– >200 simulated falls– 100% sensitivity– 9 different fall scenarios were tested

• Clinical study 2 – 48 hr monitoring (8 elderly, 65+ yrs old)– 10 adult + 8 elderly subjects– No false alarm

• Clinical study 3 – 48 hr monitoring (12 elderly, 65+ yrs old) – 12 elderly subjects– No false alarm

• Identify persons with high risk of fall• Detect early signs before falls happen• Timed Up and Go (TUG) Tests

– Stand up from chair, walk 10 feet, turn around, walk back to chair, sit down.

– Normal completion time 7-10 seconds

Measurable automaticallyduring daily activities

Risk of Falling Assessment

Pilot Study

• Clinical study 2 – 48 hr monitoring– TUG tests performed for 8 elderly subjects– 4 in high-risk* group– 4 in low-risk group

* high-risk here means completing TUG test takes longer than 15 seconds

• Sit-to-Stand posture transition time(duration of rising from chair)– high-risk group takes 124% longer

than low-risk group

Automatic Risk of Falling Assessmentvia Physical Activity Monitoring

• Posture• Gait• Balance

• Activity Pattern

• High Risk

• Low Risk

Monitored Factors for Risk of Falling Assessment

Measurements Compute for every 24H

Sit-to-stand transition duration mean & standard dev.

Stand-to-sit transition duration mean & standard dev.

Total duration of standing % of day

Total duration of walking % of day

Walking episode duration mean & standard dev.

Number of steps per walking episode mean & standard dev.

Longest walking episode no. of steps

Gait velocity mean & standard dev.

Gait initiation time time to reach stable gait

Hypothesis: High risk group and low risk group will be separated by all or a subset of the parameters above.

Ongoing Studies

• Clinical study 4 – 48 hr monitoring– University of Arizona Center on Aging, Frailty Study– 20 elderly subject

• Clinical study 5 – 24 hr monitoring– University of Arizona Center on Aging, Dementia Study– 90 elderly subject

• Clinical study 6 – 48 hr monitoring– University of Arizona Center on Aging and Dept. of Surgery– Risk of Falling Assessment Study– 180 elderly subjects

Risk of falling assessment from additional infrequent sensor measurements

Posture Balance Gait

- Multiple 9-axis IMUs- Periodic non-continuous measurements- Static and dynamic balance assessment - Measurement of gait parameters

- 3-axis accelerometer- Continuous sampling- Physical activity / falls

OOODetected Falls

Questions & Comments?bor-rong.chen@biosensics.com

Wireless wearable sensors offer a promising approach for real-time and continuous monitoring of falls and risk of falling during daily activities