Quantified Self-Using Consumer Wearable Device: Predicting Physical and Mental Health

Pardamean, Bens; Soeparno, Haryono; Budiarto, Arif; Mahesworo, Bharuno; Baurley, James W.

doi:10.4258/hir.2020.26.2.83

Cited by 25 publications

(9 citation statements)

References 41 publications

(69 reference statements)

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“…FIGURE 1 Concept of 'Layered mental healthcare'. OT is the optical topography, i.e., functional near-infrared spectroscopy device A lot of biometric measurements have been used for monitoring distress and also both bio-and neuro-feedbacks for stress coping [9]. The concept of layer is introduced here to cover a variety of preceding symptoms.…”

Section: Layered Mental Healthcarementioning

confidence: 99%

“…A lot of biometric measurements have been used for monitoring distress and also both bio‐and neuro‐feedbacks for stress coping [ 9 ]. The concept of layer is introduced here to cover a variety of preceding symptoms.…”

Section: Layered Mental Healthcarementioning

confidence: 99%

“…Furthermore, the management of working hours, conditions, and environment can be recommended to employers, depending on the employees conditions. A lot of biometric measurements have been used for monitoring distress and also both bio-and neuro-feedbacks for stress coping [9]. The concept of layer is introduced here to cover a variety of preceding symptoms.…”

Section: Layered Mental Healthcarementioning

confidence: 99%

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Proposal of layered mental healthcare for mental well‐being

Kiguchi

Sutoko

Atsumori

et al. 2021

Healthcare Tech Letters

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A new concept, 'Layered mental healthcare' for keeping employees mental well-being in the workplace to avoid losses caused by both absenteeism and presenteeism is proposed.A key factor forming the basis of the concept is the biometric measurements over three layers, i.e., behaviour, physiology, and brain layers, for monitoring mental/distress conditions of employees. Here, the necessity of measurements in three layers was validated by the data-driven approach using the preliminary dataset measured in the office environment. Biometric measurements were supported by an activity tracker, a PC logger, and the optical topography; mental/distress conditions were quantified by the brief job stress questionnaire. The biometric features obtained 1 week before the measurement of mental/distress scores were selected for the best regression model. The feature importance of each layer was obtained in the learning process of the best model using the light graded boosting machine and was compared between layers. The ratio of feature importance of behaviour:physiology:brain layers was found to be 4:3:3. The study results suggest the contribution and necessity of the three-layer features in predicting mental/distress scores.

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Section: Layered Mental Healthcarementioning

confidence: 99%

Section: Layered Mental Healthcarementioning

confidence: 99%

Section: Layered Mental Healthcarementioning

confidence: 99%

See 1 more Smart Citation

Proposal of layered mental healthcare for mental well‐being

Kiguchi

Sutoko

Atsumori

et al. 2021

Healthcare Tech Letters

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show abstract

“…Sufficient prior knowledge is also required to support the modeling process to achieve a high action recognition rate [11]. Depending on the type of features extracted, traditional-based action recognition methods can be further classified as human geometry-based methods, motion information-based methods, and spatiotemporal interest point-based methods [12]. Deep learning-based action recognition is an end-to-end approach that automatically learns relevant features directly from the original RGB video sequences and uses them for action classification and is divided into dual-stream convolutional neural networkbased action recognition methods, 3D convolutional neural network-based action recognition methods, and long-and short-term memory network-based action recognition methods depending on the network structure [13].…”

Section: Current Status Of Researchmentioning

confidence: 99%

Research on the Identification and Evaluation of Aerobics Movements Based on Deep Learning

Zhao

Wang

Fan

2021

Scientific Programming

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A deep learning approach is used in this study to provide insight into aerobics movement recognition, and the model is used for aerobics movement recognition. The model complexity is significantly reduced, while the multi-scale features of the target at the fine-grained level are extracted, significantly improving the characterization of the target, by embedding lightweight multi-scale convolution modules in 3D convolutional residual networks to increase the local perceptual field range in each layer of the network. Finally, using the channel attention mechanism, the key features are extracted from the multi-scale features. To create a dual-speed frame rate detection model, the fast-slow combination idea is fused into a 3D convolutional network. To obtain spatial semantic information and motion information in the video, the model uses different frame rates, and the two-channel information is fused with features using lateral concatenation. Following the acquisition of all features, the features are fed into a temporal detection network to identify temporal actions and to design a behavior recognition system for the network model to demonstrate the network model's applicability. The average scores of students in the experimental group were significantly higher than those in the control group in seven areas: set accuracy, movement amplitude, movement strength, body coordination, coordination of movement and music, movement expression, and aesthetics; the average scores of movement proficiency and body control in the experimental group were also significantly higher than those in the control group, but the differences were not significant. The differences between the eight indicators in the experimental group were not significant when compared to those in the preexperimental group, indicating that intensive rhythm training for students improves secondary school students' comprehension, proficiency, and presentation of aerobics sets.

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“…Wearable sensing technologies have enabled us to monitor our bodily states and, given some self-discipline, improve our health [9]. The entry device for most people, and often central hub for data analysis, is their smartphone [2].…”

Section: Introductionmentioning

confidence: 99%

Augmenting interoceptive awareness with off-the-shelf sensors using visuo-haptic emotional stimulus

et al. 2022

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Wearable sensing technologies allow us to monitor and track a wealth of information about bodily states. Tracking applications rely on abstract numerical or graphical visualizations to make this information accessible to us. However, these visualizations can be hard to interpret, and even be harmful to already vulnerable groups. Hence, we propose to give feedback in the form of an enhanced heart rate interoception and an embodied artificial agent. This method relies on the person’s inherent understanding of their own body. It is a subtle and more natural way to gauge the meaning of off-the-shelf sensors’ feedback. A wearable pet prototype that presents emotion through visuo-haptic feedback is evaluated in a match and a mismatch group. Participants in both groups answered self-report and perceived affect of the interoceptive feedback without significant differences. However, the groups’ perceived closeness to the pet differed significantly.

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Quantified Self-Using Consumer Wearable Device: Predicting Physical and Mental Health

Cited by 25 publications

References 41 publications

Proposal of layered mental healthcare for mental well‐being

Proposal of layered mental healthcare for mental well‐being

Research on the Identification and Evaluation of Aerobics Movements Based on Deep Learning

Augmenting interoceptive awareness with off-the-shelf sensors using visuo-haptic emotional stimulus

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