Characterization of a wearable system for automatic supervision of fitness exercises
Section snippets
Introduction and motivation
Being physically active on a regular basis has many positive effects on human health. A number of recent studies have investigated this connection. The causal relationship between physical activity and cardiovascular diseases, thromboembolic stroke, hypertension, type 2 diabetes mellitus, osteoporosis, obesity, and colon/breast cancer has been deeply analyzed in the last years [1]. Recently, the focus has shifted to the quantification of the dose-response connection between physical exercise
State of art and related studies
A survey in EU countries showed that only 31% of respondents reported sufficient physical activity [13]. An example is related to senior citizens, a part of the population that would greatly benefit from this kind of activity. A research found out that 87% of elderly subjects have health-related and/or lack-of-knowledge barriers [14]. As stated in the Introduction, a possible solution is the adoption of automated supervision of exercise training, made possible by the advent of low-cost
The proposed approach
The basic idea of the proposed solution is shown in Fig. 1. One or more wearable IMUs acquire the user’s movements while performing exercises. These data are then transmitted to a processing unit for data evaluation, for example through the broadly-used Bluetooth (BT) connection [33]. Following recent trends in personal systems for medical and sport/fitness support, the latter could be implemented with a smart device, such as a smartphone, tablet and smartwatch. Indeed, the term “mHealth” has
The actual implementation
The proposed system adopts a single non-invasive wrist-worn IMU, the x-IMU manufactured by x-IO technologies (http://x-io.co.uk). It hosts a tri-axial 12-bit accelerometer, a 16-bit tri-axial gyroscope and a 12-bit tri-axial magnetometer (not used in this work). The adopted sampling frequency is 256 Sa/s. An example of raw accelerometer and gyroscope data can be seen in Fig. 2.
The x-IMU sensor is equipped with a BT module and has an internal micro SD card for data storage. In the experimental
Experimental results
As previously explained, the feature reduction requires a preliminary PCA analysis in order to extract the coefficients for the linear mapping of the initial 72 features into the reduced 20 dimensional feature space. Moreover, the LDA must be trained using a reference data set. In the proposed approach, only a single reference data set has been selected for both the aforementioned tasks; in particular, the “leave-one-out” cross-validation (LOOCV) has been applied on the overall acquisitions of
Conclusions
In this paper, a new approach for automatic classification and counting of fitness exercises is proposed. In particular, PCA and LDA ML techniques have been employed in light of reducing the computational efforts. Additionally, a confidence parameter and a filtering strategy based on event-duration have been introduced, in order to improve reliability. The comprehensive performance characterization leverages on a data set obtained monitoring the physical activity of seven users with different
Funding
This work has been partially supported by Smart Cities and Communities and Social Innovation Grant D84G14000220008: “Smart Aging” and by the University of Brescia H&W grant “Breaking bad breakfast habits” and “Work Wealth Production Productivity”.
Declaration of Competing Interest
None.
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