Abstract
The smart devices used for health and physical activities monitoring are elements with high presence in the market of wearables. This work presents an estimaton method for walking speed based on a multilayer artificial neural netwok, which has been trained to obtain the ratio between this speed and the frequency of the arms motion, characteristics of each person. In spite of using only 3 input variables (hight weight and gender), errors than less than 10% were obtained for the mentioned ratio. In addition, the estimation algorith has been incorporated into a low cost, wrist weareable device, ehich uses a inertial measurement unit (IMU) to measure the angular velocity of one arm. These IMUs are not common for these type of devices, but can be used to obtain more accurate speed measures than those obtained by meas of GPS units. Thus, the system can be used to record the physical activity with higher accuracy.Keywords: Artificial neural network, walking speed measurement, health monitoring, IMU, GPS.
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