Automated Detection of Real-World Falls: Modeled From People With Multiple Sclerosis

Clara Mosquera-Lopez, Eric A. Wan, Mahesh Shastry, Peter G Jacobs

Abstract:

Falls are a major health problem with one in
three people over the age of 65 falling each year, oftentimes
causing hip fractures, disability, reduced mobility, hospitalization and death. A major limitation in fall detection algorithm development is an absence of real-world falls data.
Fall detection algorithms are typically trained on simulated
fall data that contain a well-balanced number of examples
of falls and activities of daily living. However, real-world
falls occur infrequently, making them difficult to capture
and causing severe data imbalance. People with multiple
sclerosis (MS) fall frequently, and their risk of falling increases with disease progression. Because of their high
fall incidence, people with MS provide an ideal model for
studying falls. This paper describes the development of a
context-aware fall detection system based on inertial sensors and time of flight sensors that is robust to imbalance,
which is trained and evaluated on real-world falls in people
with MS. The algorithm uses an auto-encoder that detects
fall candidates using reconstruction error of accelerometer
signals followed by a hyper-ensemble of balanced random
forests trained using both acceleration and movement features. On a clinical dataset obtained from 25 people with MS
monitored over eight weeks during free-living conditions,
54 falls were observed and our system achieved a sensitivity of 92.14%, and false-positive rate of 0.65 false alarms
per day.