@conference {442, title = {Continuous and Unconstrained Vital Signs Monitoring with Ballistocardiogram Sensors in Headrest Position}, booktitle = {IEEE-EMBS International Conferences on Biomedical and Health Informatics}, year = {2017}, month = {02/2017}, publisher = {IEEE}, organization = {IEEE}, address = {Orlando, Florida, United States}, abstract = {Unobtrusive and long-term monitoring of human vital signs are essential requirements for early diagnosis and prophylaxis due to many reasons, one of the most important being improving the quality of life. Currently, vital signs are continuously monitored through sensors attached to the body, such as multiple electrodes for measuring electrical activity of the heart. Such methods may be undesirable, especially for elderly, infants and other groups of people. In this paper, we introduce an improved technique for measuring heart rate from noisy ballistocardiogram signals acquired from 50 human volunteers in a sitting position using a massage chair. The signals are unobtrusively collected from a microbend fiber optic sensor embedded within the headrest of the chair, and then transmitted to a computer through a Bluetooth connection. The heart rate is computed using the multiresolution analysis of the maximal overlap discrete wavelet transform. The error between the proposed method and the reference ECG is estimated in beats per minute using the mean absolute error, where the system achieved relatively good results (7.31 \± 1.60) despite the large amount of motion artifacts produced owing to the frequent body movements and/or vibrations of the massage chair during stress relief massage. Unlike the complete ensemble empirical mode decomposition algorithm, previously employed for heart rate estimation, the suggested system is much faster. Hence, it can be used in real-time applications.}, keywords = {Technology and services for assisted-living and elderly, Technology and services for home care}, doi = {10.1109/BHI.2017.7897262}, url = {http://ieeexplore.ieee.org/abstract/document/7897262/}, author = {Ibrahim Sadek and Jit Biswas and Bessam Abdulrazak and Zhang Haihong and Mounir Mokhtari} }