Dear Colleagues,

Recent advances on sensor technologies and their pervasiveness allow the development of numerous applications and services to improve people’s health. This Special Issue invites papers presenting novel research on utilizing sensors placed on users’ bodies and in their environments to understand and improve their health and wellbeing. We are interested in sensing devices, methods, and applications for obtaining and analyzing different categories of data (physiological, behavioral, emotional, environmental, etc.), and utilizing different technologies such as signal processing, machine learning, etc. for empowering citizens and health professionals to take action based on the analyzed sensor data.

This Special Issue is supported by the WideHealth Project, which is a European TWINNING project (No. 952279) that supports dissemination activities on the topics of eHealth and Pervasive health, and aims to enable a new generation of researchers on the aforementioned topics.

Dr. Hristijan Gjoreski
Dr. Oscar Mayora
Dr. Mitja Luštrek
Dr. Tiago Guerreiro
Prof. Dr. Bert Arnrich
Guest Editors

Manuscript Submission Information

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• smart sensing
• pervasive health
• wearables
• machine learning
• deep learning
• sensor fusion
• human activities and behavior analytics
• physiological sensing

Planned Paper1#
Type of Paper: Research Article
Tentative Title: Walking identification in aged residential care: a computationally inexpensive and orientation independent algorithm with a single 3-axis accelerometer on the trunk
Authors: Mhairi K. MacLean1, Rana Zia Ur Rehman1, Ngaire Kerse2, Lynne Taylor2, Lynn Rochester1, Silvia Del Din1
Affiliations:1.Newcastle University, Translational and Clinical Research Institute; 2.University of Auckland, Faculty of Medical and Health Sciences

Abstract: 
Background and Aims: Measurement of walking activity in long-term residential care environments is important for understanding mobility, assessing the effects of interventions, tracking disease progression, and identifying unmet mobility needs . Previous research has shown that gait classification algorithms can identify multiple gait characteristics like walking speed, gait asymmetry, and stride length with a single 3-axis inertial measurement unit (IMU) worn on the lower back. Gait classification in a long-term residential care environment is challenging due to slow walking speeds, halting gait patterns, and reliance on walking aids which alter gait characteristics. The aim of this study was to develop a computationally inexpensive algorithm to classify gait in a residential care environment. To improve robustness of the gait classifier, we aimed to make the algorithm independent of the IMU orientation, ensuring that improper placement of the IMU would not lead to significant data loss. 
Methods: Data were collected from 27 ambulatory residents in long-term care. A clinician secured the IMU to the fifth lumbar vertebrae with a hydrogel adhesive. Participants were asked to walk around the care home, stand, and sit during a 5–15-minute period. The researcher recorded the order of activities and time taken for each activity, with sit-stand and stand-sit transfers counting as activities. We also recorded a video of the participants feet during the data collection. The IMU sampled data at 100 Hz.
Acceleration data were first adjusted by subtracting the low pass filtered data, we then transformed all 3 linear accelerations into a single magnitude signal. Thereafter, we classified each timepoint as walking or not-walking using thresholds, and removed small gaps in activity with a smoothing technique. We excluded walking bouts less than 2 seconds. We compared the algorithm classification to the clinician labelled data at each timepoint to find accuracy, sensitivity, precision, specificity, and F1 score. We averaged these scores across participants.
Results: Our algorithm performed well with an average accuracy of 79% ±19 standard deviation (s.d.). Sensitivity was also 79% (±27 s.d). Precision was 83% ±25, specificity was 82% ±11, and F1 score was 80% ±26 (mean±s.d). 
Discussion and Conclusions.We validated a computationally inexpensive and orientation independent algorithm for classifying gait in a long-term care environment. Our results show the algorithm worked relatively well across participants. The importance of an easy to use, computationally inexpensive technique to classify gait was accentuated by the ability of our algorithm to identify bouts of walking that were not labelled as such during the data collection, although the video data exhibits walking. 

Planned Paper2#

Title: "Recognising activities using ear-worn sensors and machine learning"
Authors:Matias Laporte,Davide Casnici, Martin Gjoreski,Shkurta Gashi, Silvia Santini, Marc Langheinrich
Affiliation: Università della Svizzera italiana

Abstract: Working toward context recognition for human memory augmentation systems, we developed Human Activity Recognition (HAR) machine learning pipeline using data from earable devices. In this paper, we analyze how the earables can be used to detect different types of verbal (e.g., speaking) and non-verbal (e.g., head shaking) interactions and other activities (e.g., standing still). We collected a dataset of ear-located IMU sensors from 30 participants and compared classical machine learning methods with state-of-the-art deep learning models to classify the raw data into a set of predefined activities. We explored how different parameters -- including sampling frequency, window size, and type of sensors -- influence the models’ performance. The best-performing model in the leave-one-subject-out evaluation, a spectro-temporal residual network (STResNet), achieved an F1-score of 0.69 in recognizing seven different activities: nodding, speaking, eating, staying, head shaking, walking, and walking while speaking. 
Keywords: earable computing, human activity recognition, machine learning, deep learning, human memory augmentation