Time-Critical Fall Prediction Based on Lipschitz Data Analysis and Design of a Reconfigurable Walker for Preventing Fall Injuries

Abstract

Falls may cause serious injuries to older adults, leading to a deteriorated quality of life. Currently, there is a lack of fall injury prevention devices, especially for the balance impaired population who rely on mobility aids. Here, the functionality of a walker is augmented, so that it can predict a fall in real-time and prevent fall injuries via a rapidly reconfigurable mechanism. A key challenge is real-time fall prediction, which is a time-critical decision making process. A fall must be predicted preemptively so that the system has sufficient time to deploy the injury prevention mechanism. Data are collected from human subjects undergoing diverse loss-of-balance situations while using a walker. A predictor based on multiple Long-Short Term Memory (LSTM) networks is constructed based on three novel techniques. First, diverse fall types are identified by separately learning fast and slow falls. Second, a “Timer LSTM” is constructed that estimates the time remaining before an imbalance is unrecoverable and the fall prevention mechanism must be activated. Then if time allows, additional data are collected and the possibility of a fall is further examined. This approach lowered the fall prediction false positive rate. Third, confounding cases are further analyzed using a metric of data deficiency, called the Lipschitz quotient. Additional data features that lower the Lipschitz quotients and, thereby, increase data predictability, are sought and incorporated into the original input signals. Augmenting the data further improved performance, and the best model had a 97% success rate at identifying falls at a 0.17% false positive rate. The prediction method is implemented on a novel walker-type fall prediction and prevention prototype. The walker has a small footprint for improved maneuverability, and becomes untippable when its expandable legs are deployed in the event of a predicted fall. Thus, the older adult tethered to the untippable walker is protected from a fall. This work introduces techniques to improve the performance of real-time fall predictors when limited data is available, identifies how to select fruitful features from the available sensor signals, and incorporates a fall predictor into a physical device for a rapid fall injury prevention response. This promises immense benefits for future research on improving older adult wellbeing through real-time fall protection.

Keywords

• Fall prediction
• machine learning
• mobility aid
• fall injury prevention
• Lipschitz analysis
• eldercare