(Invited) Programmable Gold Nanowire Tattoo for Multimodal Wearable Sensors

Postural sway has been demonstrated to increase following exposure to different types of motion. However, limited prior studies have investigated the relationship between exposure to normative on-road driving conditions and standing balance following the exposure. The purpose of this on-road study was to quantify the effect of vehicle motion and task performance on passengers’ post-drive standing balance performance. In this study, trunk-based kinematic data were captured while participants performed a series of balance exercises before and after an on-road driving session in real-time traffic. Postural sway for all balance exercises increased following the driving session. Performing a series of ecologically relevant visual-based tasks led to increases in most post-drive balance metrics such as sway position and velocity. However, the post-drive changes following the driving session with a task were not significantly different compared to changes observed following the driving session without a task. The post-drive standing balance performance changes observed in this study may increase vulnerable users’ risk of falling. Wearable sensors offer an opportunity to monitor postural sway following in-vehicle exposures.

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Employing entropy measures to identify visitors in multi-occupancy environments

Journal of Ambient Intelligence and Humanized Computing ◽

10.1007/s12652-020-02824-z ◽

2020 ◽

Author(s):

Aadel Howedi ◽

Ahmad Lotfi ◽

Amir Pourabdollah

Keyword(s):

Home Environment ◽

Daily Living ◽

Wearable Sensors ◽

Permutation Entropy ◽

Care Workers ◽

Entropy Measures ◽

Novel Method ◽

High Degree ◽

Motion Detectors ◽

To Receive

AbstractHuman activity recognition (HAR) is used to support older adults to live independently in their own homes. Once activities of daily living (ADL) are recognised, gathered information will be used to identify abnormalities in comparison with the routine activities. Ambient sensors, including occupancy sensors and door entry sensors, are often used to monitor and identify different activities. Most of the current research in HAR focuses on a single-occupant environment when only one person is monitored, and their activities are categorised. The assumption that home environments are occupied by one person all the time is often not true. It is common for a resident to receive visits from family members or health care workers, representing a multi-occupancy environment. Entropy analysis is an established method for irregularity detection in many applications; however, it has been rarely applied in the context of ADL and HAR. In this paper, a novel method based on different entropy measures, including Shannon Entropy, Permutation Entropy, and Multiscale-Permutation Entropy, is employed to investigate the effectiveness of these entropy measures in identifying visitors in a home environment. This research aims to investigate whether entropy measures can be utilised to identify a visitor in a home environment, solely based on the information collected from motion detectors [e.g., passive infra-red] and door entry sensors. The entropy measures are tested and evaluated based on a dataset gathered from a real home environment. Experimental results are presented to show the effectiveness of entropy measures to identify visitors and the time of their visits without the need for employing extra wearable sensors to tag the visitors. The results obtained from the experiments show that the proposed entropy measures could be used to detect and identify a visitor in a home environment with a high degree of accuracy.

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Technology assessment framework for precision health applications

International Journal of Technology Assessment in Health Care ◽

10.1017/s0266462321000350 ◽

2021 ◽

Vol 37 (1) ◽

Author(s):

M. Sazzad Hussain ◽

David Silvera-Tawil ◽

Geremy Farr-Wharton

Keyword(s):

Emerging Technologies ◽

Mobile Apps ◽

Scientific Evidence ◽

Wearable Sensors ◽

Assessment Process ◽

Assessment Framework ◽

Response Options ◽

Regulatory Standards ◽

Precision Health ◽

Health Applications

Abstract Objective Established and emerging technologies—such as wearable sensors, smartphones, mobile apps, and artificial intelligence—are shaping positive healthcare models and patient outcomes. These technologies have the potential to become precision health (PH) innovations. However, not all innovations meet regulatory standards or have the required scientific evidence to be used for health applications. In response, an assessment framework was developed to facilitate and standardize the assessment of innovations deemed suitable for PH. Methods A scoping literature review undertaken through PubMed and Google Scholar identified approximately 100 relevant articles. These were then shortlisted (n = 12) to those that included specific metrics, criteria, or frameworks for assessing technologies that could be applied to the PH context. Results The proposed framework identified nine core criteria with subcriteria and grouped them into four categories for assessment: technical, clinical, human factors, and implementation. Guiding statements with response options and recommendations were used as metrics against each criterion. Conclusion The proposed framework supports health services, health technology innovators, and researchers in leveraging current and emerging technologies for PH innovations. It covers a comprehensive set of criteria as part of the assessment process of these technologies.

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CorrNet: Fine-Grained Emotion Recognition for Video Watching Using Wearable Physiological Sensors

Sensors ◽

10.3390/s21010052 ◽

2020 ◽

Vol 21 (1) ◽

pp. 52

Author(s):

Tianyi Zhang ◽

Abdallah El Ali ◽

Chen Wang ◽

Alan Hanjalic ◽

Pablo Cesar

Keyword(s):

Emotion Recognition ◽

Electrodermal Activity ◽

Short Form ◽

Recognition Performance ◽

Binary Classification ◽

Wearable Sensors ◽

Recognition Algorithm ◽

Fine Grained ◽

Physiological Sensors ◽

Valence And Arousal

Recognizing user emotions while they watch short-form videos anytime and anywhere is essential for facilitating video content customization and personalization. However, most works either classify a single emotion per video stimuli, or are restricted to static, desktop environments. To address this, we propose a correlation-based emotion recognition algorithm (CorrNet) to recognize the valence and arousal (V-A) of each instance (fine-grained segment of signals) using only wearable, physiological signals (e.g., electrodermal activity, heart rate). CorrNet takes advantage of features both inside each instance (intra-modality features) and between different instances for the same video stimuli (correlation-based features). We first test our approach on an indoor-desktop affect dataset (CASE), and thereafter on an outdoor-mobile affect dataset (MERCA) which we collected using a smart wristband and wearable eyetracker. Results show that for subject-independent binary classification (high-low), CorrNet yields promising recognition accuracies: 76.37% and 74.03% for V-A on CASE, and 70.29% and 68.15% for V-A on MERCA. Our findings show: (1) instance segment lengths between 1–4 s result in highest recognition accuracies (2) accuracies between laboratory-grade and wearable sensors are comparable, even under low sampling rates (≤64 Hz) (3) large amounts of neutral V-A labels, an artifact of continuous affect annotation, result in varied recognition performance.

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