Non-invasive monitoring of vital signs for older adults using recliner chairs
Autor: | Moein Enayati, Mihail Popescu, Marjorie Skubic, Akshith Ullal, James M. Keller, Bo Yu Su, Laurel Despins |
---|---|
Rok vydání: | 2020 |
Předmět: |
Thorax
medicine.medical_specialty 020205 medical informatics Heartbeat business.industry Non invasive Biomedical Engineering Vital signs Bioengineering 02 engineering and technology Health outcomes Accelerometer Applied Microbiology and Biotechnology 03 medical and health sciences 0302 clinical medicine Physical medicine and rehabilitation Statistical significance Back problems 0202 electrical engineering electronic engineering information engineering Medicine 030212 general & internal medicine business Biotechnology |
Zdroj: | Health and Technology. 11:169-184 |
ISSN: | 2190-7196 2190-7188 |
DOI: | 10.1007/s12553-020-00503-9 |
Popis: | In-home monitoring has the potential to track health changes for older adults with chronic health conditions, thereby making early treatment possible when exacerbations arise. A recliner chair is often used by older adults for sleeping at night, especially by those with breathing difficulty, neck and back problems. Here, we present a sensor system for recliner chairs that can be used to monitor heart and respiration rates. The system uses two accelerometers placed strategically to capture these vital signs noninvasively and without direct contact with the body, while at the same time, being hidden from view. The system was tested with 45 subjects, having an average age of 78.8 (S.D. = 12.5) years, for both upright and reclined configurations of the chair. We also tested the system on 6 different types of recliner models. The ground truth signal for the heart and respiratory rates were obtained using a piezoelectric finger transducer and thorax chest belt respectively. The mean heartbeat error for 45 subjects was 0.6 ms with an average error rate of 3.6% (p value = 0.00081, significance level = 0.05). Similarly, the mean respiratory breath error was 4.2 ms with an average detection error rate of 6.25% (p value = 0.032, significance level = 0.05). An analysis of the error rates are considered and possible explanations are suggested. This new system has the potential to help in identifying health changes very early, thereby facilitating early treatment and improved health outcomes for older adults. |
Databáze: | OpenAIRE |
Externí odkaz: |