On-body device localization for health and medical monitoring applications

Abstract

Abstract-We present a technique to discover the position of sensors on the human body. Automatic on-body device localization ensures correctness and accuracy of measurements in health and medical monitoring systems. In addition, it provides opportunities to improve the performance and usability of ubiquitous devices. Our technique uses accelerometers to capture motion data to estimate the location of the device on the user's body, using mixed supervised and unsupervised time series analysis methods. We have evaluated our technique with extensive experiments on 25 subjects. On average, our technique achieves 89% accuracy in estimating the location of devices on the body.

Sensing and Classifying Impairments of GPS Reception on Mobile Devices

“…Worth investigating is also the incorporation of indicators for the user's phone handling as well as his context, e.g., his transportation mode, which are provided through data from sensors other than the GPS. E.g., Vahdatpour et al [23] propose to detect a device's on-body placement from accelerometer readings.…”

Fine-grained Accelerometer-based Smartphone Carrying States Recognition during Walking

“…On-body position sensing is getting attention to researchers in machine learning and ubiquitous computing communities [7], [25], [29]. A research direction is on the type of a device which is actually realized or intended to be utilized in the future as wearable devices [18], [21], [29] or a smartphone [1], [5]- [7], [12], [14], [22], [25], [30].…”

“…A research direction is on the type of a device which is actually realized or intended to be utilized in the future as wearable devices [18], [21], [29] or a smartphone [1], [5]- [7], [12], [14], [22], [25], [30]. The type of a device relates to the selection of target positions.…”

“…The type of a device relates to the selection of target positions. In the wearable device approach, the target positions range from the head to the ankle including fine-grained discrimination such as upper arm vs. forearm and shin vs. thigh [29]. A device is usually attached firmly using a belt or a special mounting fixture.…”

“…Regarding the evaluation method, n-fold cross validation is often utilized [1], [12], [18], [22], [25], [29], which utilizes (n-1)/n of dataset for training a classifier and 1/n for testing the classifier; it tends to result in good recognition performance because the training dataset may contain (n-1)/n of data from each person in theory, and hence the classifier "knows" about the subjects in advance. By contrast, Leave-One-Subject-Out (LOSO) cross validation is carried out by testing a dataset from a particular person with a classifier that is trained without a dataset from the person.…”