Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions

The Intrusiveness of Sensor-Suit Components on the Postures Associated with Performing Repeated Whole-Body Manual Lifting Tasks

Ergonomics can be defined as the responsibility to ensure that the demands of a job do not exceed the capabilities of a worker (Garg, Chaffin, and Herrin, 1978). Evaluating physiological demands on workers, particularly individuals performing highly varied tasks or monitoring their work in the field, can be a complex problem. Using on-person sensors to record kinematic and physiological measurements throughout extended is one proposed method by which to collect data necessary to evaluate the demands placed on the workers. In order to assess the efficacy of the data that would be collected, it is critical to evaluate the intrusive effects of the on-person sensors on the manner in which the work is performed. For the purpose of this study, various outfit ensembles, consisting of combinations of Clo, Mass, and Banding, were analyzed in order to determine whether or not they affect the posture while individuals perform a repetitive lifting task. Thirty-two paid volunteers participated in this study. Each subject was randomly assigned one of eight experimental ensembles. VICON MX-13 near-infrared cameras were used to capture whole-body kinematics. Subjects were asked to perform a cyclic work protocol that consisted of six twenty-minute cycles, including five-minutes each of 30 ground-to-waist lifts and lowers, 30 standing arm lifts and lowers, continuous walking on a treadmill, and a rest period. A 23 factorial, between-subjects design was used, producing 8 experimental ensemble conditions for combinations of Clo, Mass, and Banding. Statistical analyses were performed with a stepwise regression and general linear model (GLM). The bilateral angles for the hips, knees, and ankles were the dependent variables; independent variables were Banding, Clo, Mass, Part, Phase, and Cycle. From the stepwise regression (alpha=0.10 to remove), Part and Phase were removed from the model. From the GLM, the adjusted R2 values indicated that a good fit existed between the variables in the model. ANOVA results indicated that the main effects and all interactions effects of the ensemble conditions were significant, but the significance varied across lower body joints. Results indicate that Banding and Clo have significant effects on posture, but their effects are less than the nominally fatiguing aspect of the tasks performed. As expected, adding mass to the subjects caused significant changes to their posture over time, suggesting elevated levels of fatigue. Future studies should include other populations, fitness and experience constraints, and tasks with a lower physiological burden

Design and Application of Wireless Body Sensors

Hörmann T. Design and Application of Wireless Body Sensors. Bielefeld: Universität Bielefeld; 2019

Context-awareness for mobile sensing: a survey and future directions

The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions

Automatic identification of physical activity intensity and modality from the fusion of accelerometry and heart rate data

Background: Physical activity (PA) is essential to prevent and to treat a variety of chronic diseases. The automated detection and quantification of PA over time empowers lifestyle interventions, facilitating reliable exercise tracking and data-driven counseling. Methods: We propose and compare various combinations of machine learning (ML) schemes for the automatic classification of PA from multi-modal data, simultaneously captured by a biaxial accelerometer and a heart rate (HR) monitor. Intensity levels (low/moderate/vigorous) were recognized, as well as for vigorous exercise, its modality (sustained aerobic/resistance/mixed). In total, 178.63 h of data about PA intensity (65.55% low/18.96% moderate/15.49% vigorous) and 17.00 h about modality were collected in two experiments: one in free-living conditions, another in a fitness center under controlled protocols. The structure used for automatic classification comprised: a) definition of 42 time-domain signal features, b) dimensionality reduction, c) data clustering, and d) temporal filtering to exploit time redundancy by means of a Hidden Markov Model (HMM). Four dimensionality reduction techniques and four clustering algorithms were studied. In order to cope with class imbalance in the dataset, a custom performance metric was defined to aggregate recognition accuracy, precision and recall. Results: The best scheme, which comprised a projection through Linear Discriminant Analysis (LDA) and k-means clustering, was evaluated in leave-one-subject-out cross-validation; notably outperforming the standard industry procedures for PA intensity classification: score 84.65%, versus up to 63.60%. Errors tended to be brief and to appear around transients. Conclusions: The application of ML techniques for pattern identification and temporal filtering allowed to merge accelerometry and HR data in a solid manner, and achieved markedly better recognition performances than the standard methods for PA intensity estimation