Krenzel, DevonWarren, StevenLi, KejiaNatarajan, BalaSingh, Gurdip2013-09-182013-09-182013-09-18http://hdl.handle.net/2097/16466Accidental slips and falls due to decreased strength and stability are a concern for the elderly. A method to detect and ideally predict these falls can reduce their occurrence and allow these individuals to regain a degree of independence. This paper presents the design and assessment of a wireless, wearable device that continuously samples accelerometer and gyroscope data with a goal to detect and predict falls. Lyapunov-based analyses of these time series data indicate that wearer instability can be detected and predicted in real time, implying the ability to predict impending incidents.en-USThis Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).AccelerometerGyroscopeWearable devicesZigBee wirelessLyapunov exponentsAndroid smart phoneText