Possibility of Detecting Changes in Health Conditions using an Improved 2D Array Sensor System

For improved detection of changes in body conditions, herein, we propose a two-dimensional system, wherein sensors are placed parallel and perpendicular to the direction of walking, based on an already proposed system that employs pressure sensors. The sensors placed parallel to the direction of wal...

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Bibliographic Details
Published in:Sensors & transducers 2022-10, Vol.259 (5), p.29-36
Main Authors: Uchida, Yasutaka, Funayama, Tomoko, Hori, Kazuyoshi, Yuge, Misako, Shinozuka, Nobuko, Kogure, Yoshiaki
Format: Article
Language:English
Subjects:
Aging
Balance
Blood pressure
Change detection
Diabetes
Diabetic retinopathy
Falls
Gait
Hemodialysis
Machine learning
Monitoring systems
Muscle strength
Older people
Patients
Power supply
Pressure sensors
Prevention
Sensor arrays
Sensors
Surveillance
Walking
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Summary:For improved detection of changes in body conditions, herein, we propose a two-dimensional system, wherein sensors are placed parallel and perpendicular to the direction of walking, based on an already proposed system that employs pressure sensors. The sensors placed parallel to the direction of walking identify the foot that steps on the sensors, and two others pairs of sensors are placed at positions corresponding to the inner and outer sides of the left and right feet, respectively, to accurately detect the foot that steps on the sensors during walking. This improved two-dimensional health monitoring system is applied to a hemodialysis patient known to have a wobble problem before and after treatment. This is performed to obtain effective values as features for machine learning. Using data obtained from the additional sensors, we develop an index that can evaluate a patient's sense of balance. The results indicate that classification is possible based on the walking speed obtained from the pressure sensors installed orthogonally to the direction of travel and the devised balance index. Using these values, the K-means method, which does not use supervised data, can be used to classify the subjects into three regions, and it is demonstrated that changes in gait before and after hemodialysis can be detected, although at an early stage.
ISSN:2306-8515
1726-5479