The World Health Organization recognizes physical activity as an influencing domain on quality of life. Monitoring, evaluating, and supervising it by wearable devices can contribute to the early detection and progress assessment of diseases such as Alzheimer’s, rehabilitation, and exercises in telehealth, as well as abrupt events such as a fall. In this work, we use a non-invasive and non-intrusive flexible wearable device for 3D spine pose measurement to monitor and classify physical activity. We develop a comprehensive protocol that consists of 10 indoor, 4 outdoor, and 8 transition states activities in three categories of static, dynamic, and transition in order to evaluate the applicability of the flexible wearable device in human activity recognition. We implement and compare the performance of three neural networks: long short-term memory (LSTM), convolutional neural network (CNN), and a hybrid model (CNN-LSTM). For ground truth, we use an accelerometer and strips data. LSTM reached an overall classification accuracy of 98% for all activities. The CNN model with accelerometer data delivered better performance in lying down (100%), static (standing = 82%, sitting = 75%), and dynamic (walking = 100%, running = 100%) positions. Data fusion improved the outputs in standing (92%) and sitting (94%), while LSTM with the strips data yielded a better performance in bending-related activities (bending forward = 49%, bending backward = 88%, bending right = 92%, and bending left = 100%), the combination of data fusion and principle components analysis further strengthened the output (bending forward = 100%, bending backward = 89%, bending right = 100%, and bending left = 100%). Moreover, the LSTM model detected the first transition state that is similar to fall with the accuracy of 84%. The results show that the wearable device can be used in a daily routine for activity monitoring, recognition, and exercise supervision, but still needs further improvement for fall detection.
Background: Annually and globally, cardiovascular diseases yield the death of 17.9 million people. Continuous and unobtrusive monitoring of vital signs supports an early detection of abnormalities and diseases, such as atrial fibrillation. Here, we analyze capacitive electrocardiography (cECG) recorded in an armchair at home. However, processing such data is challenging, as body movements and other artifacts disturb the signal quality. Methods: In this paper, we suggest video-based pose estimation to assess the reliability of cEGC. In 20 subjects, we measured reference and capacitive ECG synchronized with a video recording for key-point-based movement analysis with the OpenPifPaf pose estimation algorithm. We considered all 17 human body joints to compute a movement index and label all data in windows of 5 s as reliable vs. unreliable, according to that index. Then, we compared the heart rates obtained from complete and reliable cEGC windows with the corresponding windows from the reference ground truth ECG. Result: The left and right hip joints are most significantly influencing the signal’s quality. In addition, the joints’ movement distance from the original position limited to the range 460.84 pixels and 382.22 pixels, respectively, deliver a reliable cECG signal. Conclusion: Video-based pose estimation delivers reliable and unreliable periods of cECG recordings and improves continuous health monitoring at home.
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