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BACKGROUND: Comprehensive satisfaction in life may be considered as a significant contributor to health for everyone, including the aging population (individuals aged 45 years and above). For understanding the comprehensive satisfaction, an assessment measure with various psychometric properties may be useful. During a longitudinal study of aging and geriatric mental health, a 26-item tool was developed in Hindi for the assessment of satisfaction. This article aimed to analyze the items of Comprehensive Satisfaction Index (ComSI) applying Varimax rotation and to find out its association with World Health Organization Quality of Life Brief (WHOQOL-BREF). METHODS: Data of 260 subjects were extracted from the longitudinal study to analyze the psychometric properties of the tool named as Comprehensive Satisfaction Index and its association with various domains of WHOQOL-BREF. Varimax rotation was applied after computing Kaiser-Meyer-Olkin and Bartlett's test of sphericity. Furthermore, the association between various components of ComSI and various domains of WHOQOL-BREF was explored. RESULTS: Of the total 26 items of the tool, item no. 17 was excluded due to its -ve/ <0.31 value. A total of three components were generated with >1 eigenvalues; maximum items were loaded in component 1 (19) followed by components 2 (4) and 3 (2). Each of these factors has been significantly correlated with each other. Furthermore, these components also were compared with various domains of WHOQOL-BREF, and positive correlation was obtained for most of them. CONCLUSION: There is a positive association between ComSI and WHOQOL-BREF. This tool will help in identifying the satisfaction level of the aging subjects promptly and efficiently, which would further help in making strategies for interventions.

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This study examined the feasibility of a non-laboratory approach that uses machine learning on multimodal sensor data to predict relative physical activity (PA) intensity. A total of 22 participants completed up to 7 PA sessions, where each session comprised 5 trials (sitting and standing, comfortable walk, brisk walk, jogging, running). Participants wore a wrist-strapped sensor that recorded heart-rate (HR), electrodermal activity (Eda) and skin temperature (Temp). After each trial, participants provided ratings of perceived exertion (RPE). Three classifiers, including random forest (RF), neural network (NN) and support vector machine (SVM), were applied independently on each feature set to predict relative PA intensity as low (RPE ≤ 11), moderate (RPE 12-14), or high (RPE ≥ 15). Then, both feature fusion and decision fusion of all combinations of sensor modalities were carried out to investigate the best combination. Among the single modality feature sets, HR provided the best performance. The combination of modalities using feature fusion provided a small improvement in performance. Decision fusion did not improve performance over HR features alone. A machine learning approach using features from HR provided acceptable predictions of relative PA intensity. Adding features from other sensing modalities did not significantly improve performance.

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This paper proposes the use of posterior-adapted class-based weighted decision fusion to effectively combine multiple accelerometer data for improving physical activity recognition. The cutting-edge performance of this method is benchmarked against model-based weighted fusion and class-based weighted fusion without posterior adaptation, based on two publicly available datasets, namely PAMAP2 and MHEALTH. Experimental results show that: 1) posterior-adapted class-based weighted fusion outperformed model-based and class-based weighted fusion; 2) decision fusion with two accelerometers showed statistically significant improvement in average performance compared to the use of a single accelerometer; 3) generally, decision fusion from three accelerometers did not show further improvement from the best combination of two accelerometers; and 4) a combination of ankle and wrist located accelerometers showed the best overall performance compared to any combination of two or three accelerometers.

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PURPOSE: To investigate whether the use of ensemble learning algorithms improve physical activity recognition accuracy compared to the single classifier algorithms, and to compare the classification accuracy achieved by three conventional ensemble machine learning methods (bagging, boosting, random forest) and a custom ensemble model comprising four algorithms commonly used for activity recognition (binary decision tree, k nearest neighbor, support vector machine, and neural network). METHODS: The study used three independent data sets that included wrist-worn accelerometer data. For each data set, a four-step classification framework consisting of data preprocessing, feature extraction, normalization and feature selection, and classifier training and testing was implemented. For the custom ensemble, decisions from the single classifiers were aggregated using three decision fusion methods: weighted majority vote, naïve Bayes combination, and behavior knowledge space combination. Classifiers were cross-validated using leave-one subject out cross-validation and compared on the basis of average F1 scores. RESULTS: In all three data sets, ensemble learning methods consistently outperformed the individual classifiers. Among the conventional ensemble methods, random forest models provided consistently high activity recognition; however, the custom ensemble model using weighted majority voting demonstrated the highest classification accuracy in two of the three data sets. CONCLUSIONS: Combining multiple individual classifiers using conventional or custom ensemble learning methods can improve activity recognition accuracy from wrist-worn accelerometer data.

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