The Wearable Multimodal Monitoring System: A Platform to Study Falls and Near-Falls in the Real-World

Abstract

Falls are particularly detrimental and prevalent in the aging population. To diagnose the cause of a fall current medical practice relies on expensive hospital admissions with many bulky devices that only provide limited diagnostic information. By utilizing the latest wearable technology, the Wearable Multimodal Monitoring System (WMMS) presented here offers a better solution to the problem of fall diagnostics and has the potential to predict these falls in real-time in order to prevent falls or, at least, mitigate their severity. This highly integrated system has been designed for real-life long-term monitoring of movement disorder patients. It contains multiple wearable and wireless biosensors that simultaneously and continuously monitor cardiovascular, autonomic, motor, and electroencephalographic (EEG) activity, in addition to receiving critical patient feedback about symptoms. Initial pilot data show that the system is comfortable and easy to use, and provides high quality data streams capable of detecting near-falls and other motor disturbances.