Activity detection using frequency analysis and off-the-shelf devices: Fall detection from accelerometer data

Abstract

Increasingly, applications of technology are being developed to provide care to elderly and vulnerable people living alone. This paper looks at using sensors to monitor a person's wellbeing. The paper attempts to recognise and distinguish falling, sitting and walking activities from accelerometer data. Fast Fourier Transformation (FFT) is used to extract information from collected data. The low-cost accelerometer is part of a Texas Instruments watch. Our experiments focus on lower sampling rates than those used elsewhere in the literature. We show that a sampling rate of 10Hz from a wrist-worn device does not reliably distinguish between a fall and merely sitting down.

Real-Time Human Ambulation, Activity, and Physiological Monitoring: Taxonomy of Issues, Techniques, Applications, Challenges and Limitations

“…For example, for the measurement of human ambulation and ADL scenarios using accelerometers, samples were acquired and recorded at frequencies of 7 Hz [ 45 ], 10 Hz [ 46 ], and 50 Hz [ 37 ], which were deemed to be a good trade-off between saving energy and acquiring enough signal data. Accelerometer signal samples obtained at 10 Hz should be fast enough to capture the necessary amount of data, yet slow enough not to capture unnecessary noise and anomalies [ 47 ]. In [ 48 ], the authors sampled their accelerometer signals at 50 Hz, while researchers in [ 33 ] obtained their accelerometer samples data at the frequency of 200 Hz.…”

FALL DETECTION USING BIOLOGIALLY INSPIRED MONITORING - Artificial Immune System in the Area of Assisted Living

“…This long term monitoring cannot only be used for behaviour analysis but also to improve fall detection. Previously, we investigated intelligent fall detection for elderly people (ADL in Figure 2) using Fast Fourier-Transformation (Bersch et al, 2011) and neural networks to successfully detect falls. This position paper makes a case to use Artificial Immune System as a new way for adaptive long term monitoring.…”

Inexpensive and Power-Efficient Wireless Health Monitoring System for the Aging Population

“…[12] evaluated accelerometers placed at waist, wrist and head and concluded that wrist was not optimal for fall detection. [27] argued that a wrist-worn accelerometer could not reliably distinguish falling and sitting down.…”