Abstract
With the increasing use of wearable devices equipped with various sensors, information on human activities, biometrics, and surrounding environments can be obtained via sensor data at any time and place. When such devices are attached to arbitrary body parts and multiple devices are used to capture body-wide movements, it is important to estimate where the devices are attached. In this study, we propose a method that estimates the load positions of wearable devices without requiring the user to perform specific actions. The proposed method estimates the time difference between a heartbeat obtained by an ECG sensor and a pulse wave obtained by a pulse sensor, and it classifies the pulse sensor position from the estimated time difference. Data were collected at 12 body parts from four male subjects and one female subject, and the proposed method was evaluated in both user-dependent and user-independent environments. The average F-value was 1.0 when the number of target body parts was from two to five.
Highlights
Wearable devices equipped with various sensors are becoming increasingly popular.The sensors embedded in such devices can obtain biometric data and information about human activities and the surrounding environments at any time and place
The proposed load position estimation method does not require users to perform any specific activities: it can identify sensor load positions when a user stands still for 10 s; We assume that the sensor device whose load position is to be identified is equipped with a pulse sensor, which consists of an infrared LED and a photoreflector
To estimate the load position, the proposed method calculates the distance between the set of time differences collected at each position in advance, which constitutes the training data, and the set of time differences obtained at unknown positions, which are the test data
Summary
Wearable devices equipped with various sensors are becoming increasingly popular. The sensors embedded in such devices can obtain biometric data and information about human activities and the surrounding environments at any time and place. As for the second category, studies on load position identification classify sensor load positions into predefined body parts [13,14] These approaches require a certain amount of data as input to a classification model, and users must perform specific activities, such as walking for a few minutes. The proposed load position estimation method does not require users to perform any specific activities: it can identify sensor load positions when a user stands still for 10 s; We assume that the sensor device whose load position is to be identified is equipped with a pulse sensor, which consists of an infrared LED and a photoreflector.
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