Fall risk management through personalised machine learning in wearables

Fall risk management is an integral part of patient care and is essential for patient safety and fall reduction in healthcare settings. Falls, specifically among the elderly and those with chronic conditions, are a leading cause of hospitalisation and contribute to increased healthcare costs1. Wearable devices can play a key role in detecting falls and providing alerts for immediate assistance2-3. Personalised machine learning (ML) models, tailored to different fall types and individual physiologies observed in real-world settings, can enhance fall detection, prediction and prevention1-6. Can wrist-worn wearable devices accurately enable the detection of falls across diverse clinical populations in a real-world setting? Real-world data were collected, by Owlytics, from a diverse population of 449 individuals; 269 Women (Age:71.7±18.7years), 152 Men (66.4±21.9years), 11 Non-specified (77.3±15.3years), over two years. The cohort consisted of the elderly, some with chronic conditions (e.g. Parkinson’s Disease), younger individuals with Multiple Sclerosis, and young healthy individuals. For each individual, 25 Hz accelerometer sensor data and 1 Hz physiological metrics were collected from wrist-worn, LifeQ-enabled wearable devices during activities of daily living (ADL) for continuous periods ranging between 1 week and 2 years. Events were classified using a cloud-based fall detection model and a designated caregiver was alerted. The caregiver confirmed or dismissed the event. From the data, accelerometer triggers were identified where the total acceleration was greater than a specified threshold. Samples were extracted containing 8 seconds of data around triggers. A curated dataset was created with 244 (150 real-life, 94 simulated) true falls and 627,693 ADL events that resembled a fall. Simulated falls were included for model training but only true falls were used for model evaluation. Fig. 1 Current fall detection model with the feedback loop to improve personalisationDownload : Download high-res image (130KB)Download : Download full-size image On a validation set consisting of 30 true falls and 125,543 ADL events, we achieved an Area Under Curve (AUC) of 0.99. At a target recall of 0.80, the false-positive rate was 0.017. Testing the model on the wrist-worn data from UMA-Fall7 dataset resulted in AUC of 0.98 and F1-score of 88.5, a substantial improvement compared to previous state-of-the-art (F1=81.5) 2. This fall detection solution is currently enabling timely care and assistance to individuals, easing the burden in clinical settings by accurately detecting falls across different populations with minimal false positives. Feedback by caregivers allows for continuous dataset expansion and personalised models. Model performance is expected to improve with increased data, expanded time-windows around fall triggers, and incorporation of physiological features. Moving the fall detection components to the edge can further decrease the interval between fall detection and caregiver alert. These personalised solutions may inform preventative rehabilitation (balance and strength training), manage the risk of falls, and enhance the overall quality of life as individuals age.