Recognition of Sedentary Behavior by Machine Learning Analysis of Wearable Sensors during Activities of Daily Living for Telemedical Assessment of Cardiovascular Risk

Abstract

With the recent advancement in wearable computing, sensor technologies, and data processing approaches, it is possible to develop smart clothing that integrates sensors into garments. The main objective of this study was to develop the method of automatic recognition of sedentary behavior related to cardiovascular risk based on quantitative measurement of physical activity. The solution is based on the designed prototype of the smart shirt equipped with a processor, wearable sensors, power supply and telemedical interface. The data derived from wearable sensors were used to create feature vector that consisted of the estimation of the user-specific relative intensity and the variance of filtered accelerometer data. The method was validated using an experimental protocol which was designed to be safe for the elderly and was based on clinically validated short physical performance battery (SPPB) test tasks. To obtain the recognition model six classifiers were examined and compared including Linear Discriminant Analysis, Support Vector Machines, K-Nearest Neighbors, Naive Bayes, Binary Decision Trees and Artificial Neural Networks. The classification models were able to identify the sedentary behavior with an accuracy of 95.00% ± 2.11%. Experimental results suggested that high accuracy can be obtained by estimating sedentary behavior pattern using the smart shirt and machine learning approach. The main advantage of the developed method to continuously monitor patient activities in a free-living environment and could potentially be used for early detection of increased cardiovascular risk.

Cardiovascular Disease Prediction from Electrocardiogram by using Machine Learning Method: A Snapshot from the Subjects of the Malaysian Cohort

“…Training and classification performance of all six classifiersDiscussionIn the current study, waveform of ECG can predict risk of CVD with the help of ML. The prediction finding is supported by another study of ML techniques which were LDA, SVM, kNN and ANN can produce an accuracy of 95.00% ± 2.11% for detection of increased CVD risk from wearable ECG[33].An additional study from multi-ethnic 6815 participants shows models from ML technique better than AHA/ASCVD and Framingham risk scores for incident CVD prediction[34]. A different study from Istanbul Cerrahpasa Medical Faculty Hospital, Istanbul found that with the help of ML, higher accuracy of CVD monitoring can be produced compared without ML classification technique[35].…”

Towards The Internet of Smart Clothing: A Review on IoT Wearables and Garments for Creating Intelligent Connected E-Textiles

“…Sedentariness is a dangerous habit that may harm wellness [216] and that has been analyzed by different researchers in order to prevent it through smart clothing. A recent example is described in [217], which presents a smart t-shirt that embeds physiological, ambient and motion sensors in order to detect sedentary lifestyle with the aid of machine learning techniques. Other authors detailed similar systems to detect physical activity, but based on a pair of modified glasses that are also able to estimate food intake [218].…”

Maximising the Opportunities of Artificial Intelligence for People Living With Cancer

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