Key result
A ubiquitous health monitoring system using a 3D accelerometer and ECG improved human movement estimation by applying normalization and the one nearest neighbor method compared to a threshold method.
A ubiquitous health monitoring system using 3D accelerometry and ECG can estimate human movements using a one nearest neighbor method to potentially alert for heart attack risk.
May support wearable cardiac monitoring development; leaves open prospective validation before any clinical use.
We are developing an ubiquitous system for constant monitoring of human health that uses a 3D accelerometer and an electrocardiogram communicating wirelessly to a personal computer. The should provide an automatic alert whenever a person's condition indicates a risk of heart attack or other danger. We collected acceleration data and bodily movement records from our staffs and from volunteer in order to determine from the movement data what sorts of movements, such as walking or riding a bicycle, a person is engaging in. Our system is able to infer the type of human movements from the acceleration data and use this in combination with ECG data to decide whether to issue an alert. Recently, we used a threshold method for the estimating what types of movements were being detected. Then we recently improved on our movement estimates by using normalization and the one nearest neighbor method.
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Kozaki et al. (2010) studied Risk of heart attack or other danger. One nearest neighbor method with normalization vs. Threshold method was evaluated on Estimation of human movement types. A ubiquitous health monitoring system using a 3D accelerometer and ECG improved human movement estimation by applying normalization and the one nearest neighbor method compared to a threshold method.
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