PROJECT TITLE :
Step Detection and Parameterization for Gait Assessment Using a Single Waist-Worn Accelerometer
One amongst the key reasons why the elderly lose their ability to live independently at home is that the decline in gait performance. A measure to assess gait performance using accelerometers is step counting. The most drawback with most step detection algorithms is the loss of accuracy at low speeds ( zero.eight m/s) that limits their use in frail elderly populations. During this paper, a step detection algorithm was developed and validated using data from ten healthy adults and 21 institutionalized seniors, predominantly frail older adults. Data were recorded using a single waist-worn triaxial accelerometer as every of the themes performed one ten-m-walk trial. The algorithm demonstrated high mean sensitivity (99 1%) for gait speeds between 0.a pair of–1.five m/s. False positives were evaluated with a series of motion activities performed by one subject. These activities simulate acceleration patterns just like those generated near the body's center of mass whereas walking in terms of amplitude signal and periodicity. Cycling was the activity which led to a higher range of false positives. By applying template matching, we have a tendency to reduced by the number of false positives within the cycling activity and eliminated all false positives in the remainder of activities. Using K-suggests that clustering, we obtained two totally different characteristic step patterns, one for traditional and one for frail walking, where specific gait events related to limb impacts and muscle flexions were recognized. The proposed system can facilitate to spot seniors at high risk of practical decline and monitor the progress of patients undergoing exercise therapy interventions.
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