scholarly journals SensorHub: Multimodal Sensing in Real-Life Enables Home-Based Studies

Sensors ◽  
2022 ◽  
Vol 22 (1) ◽  
pp. 408
Author(s):  
Jonas Chromik ◽  
Kristina Kirsten ◽  
Arne Herdick ◽  
Arpita Mallikarjuna Kappattanavar ◽  
Bert Arnrich
Keyword(s):  
Data Collection ◽  
Observational Studies ◽  
Electrodermal Activity ◽  
Real Life ◽  
Sensor Data ◽  
Rigorous Approach ◽  
Additional Information ◽  
Multiple Sensors ◽  
Multimodal Sensing ◽  
Home Based

Observational studies are an important tool for determining whether the findings from controlled experiments can be transferred into scenarios that are closer to subjects’ real-life circumstances. A rigorous approach to observational studies involves collecting data from different sensors to comprehensively capture the situation of the subject. However, this leads to technical difficulties especially if the sensors are from different manufacturers, as multiple data collection tools have to run simultaneously. We present SensorHub, a system that can collect data from various wearable devices from different manufacturers, such as inertial measurement units, portable electrocardiographs, portable electroencephalographs, portable photoplethysmographs, and sensors for electrodermal activity. Additionally, our tool offers the possibility to include ecological momentary assessments (EMAs) in studies. Hence, SensorHub enables multimodal sensor data collection under real-world conditions and allows direct user feedback to be collected through questionnaires, enabling studies at home. In a first study with 11 participants, we successfully used SensorHub to record multiple signals with different devices and collected additional information with the help of EMAs. In addition, we evaluated SensorHub’s technical capabilities in several trials with up to 21 participants recording simultaneously using multiple sensors with sampling frequencies as high as 1000 Hz. We could show that although there is a theoretical limitation to the transmissible data rate, in practice this limitation is not an issue and data loss is rare. We conclude that with modern communication protocols and with the increasingly powerful smartphones and wearables, a system like our SensorHub establishes an interoperability framework to adequately combine consumer-grade sensing hardware which enables observational studies in real life.

scholarly journals Comparison of delayless digital filtering algorithms and their application to multi-sensor signal processing

2018 ◽  
Vol 41 (8) ◽  
pp. 2338-2351 ◽  
Author(s):  
Anna Swider ◽  
Eilif Pedersen
Keyword(s):  
Signal Processing ◽  
Fourier Transformation ◽  
Filter Design ◽  
Real Life ◽  
Digital Filtering ◽  
Data Preprocessing ◽  
Sensor Data ◽  
Discrete Fourier Transformation ◽  
Filtering Algorithms ◽  
Multiple Sensors

In the phase of industry digitalization, data are collected from many sensors and signal processing techniques play a crucial role. Data preprocessing is a fundamental step in the analysis of measurements, and a first step before applying machine learning. To reduce the influence of distortions from signals, selective digital filtering is applied to minimize or remove unwanted components. Standard software and hardware digital filtering algorithms introduce a delay, which has to be compensated for to avoid destroying signal associations. The delay from filtering becomes more crucial when the analysis involves measurements from multiple sensors, therefore in this paper we provide an overview and comparison of existing digital filtering methods with an application based on real-life marine examples. In addition, the design of special-purpose filters is a complex process and for preprocessing data from many sources, the application of digital filtering in the time domain can have a high numerical cost. For this reason we describe discrete Fourier transformation digital filtering as a tool for efficient sensor data preprocessing, which does not introduce a time delay and has low numerical cost. The discrete Fourier transformation digital filtering has a simpler implementation and does not require expert-level filter design knowledge, which is beneficial for practitioners from various disciplines. Finally, we exemplify and show the application of the methods on real signals from marine systems.

scholarly journals Real-Time Monitoring of Passenger’s Psychological Stress

2019 ◽  
Vol 11 (5) ◽  
pp. 102
Author(s):  
Gaël Vila ◽  
Christelle Godin ◽  
Oumayma Sakri ◽  
Etienne Labyt ◽  
Audrey Vidal ◽  
...  
Keyword(s):  
Real Time ◽  
Electrodermal Activity ◽  
High Stress ◽  
Ambulatory Monitoring ◽  
Real Life ◽  
Smartphone Application ◽  
Physiological Signals ◽  
Sensor Data ◽  
Moderate Activity ◽  
Stress Levels

This article addresses the question of passengers’ experience through different transport modes. It presents the main results of a pilot study, for which stress levels experienced by a traveller were assessed and predicted over two long journeys. Accelerometer measures and several physiological signals (electrodermal activity, blood volume pulse and skin temperature) were recorded using a smart wristband while travelling from Grenoble to Bilbao. Based on user’s feedback, three events of high stress and one period of moderate activity with low stress were identified offline. Over these periods, feature extraction and machine learning were performed from the collected sensor data to build a personalized regressive model, with user’s stress levels as output. A smartphone application has been developed on its basis, in order to record and visualize a timely estimated stress level using traveler’s physiological signals. This setting was put on test during another travel from Grenoble to Brussels, where the same user’s stress levels were predicted in real time by the smartphone application. The number of correctly classified stress-less time windows ranged from 92.6% to 100%, depending on participant’s level of activity. By design, this study represents a first step for real-life, ambulatory monitoring of passenger’s stress while travelling.

scholarly journals Augmenting Social Science Research with Multimodal Data Collection: The EZ-MMLA Toolkit

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 568
Author(s):  
Bertrand Schneider ◽  
Javaria Hassan ◽  
Gahyun Sung
Keyword(s):  
Data Collection ◽  
Social Science Research ◽  
Science Research ◽  
Sensor Data ◽  
Easy Access ◽  
Classroom Context ◽  
Multimodal Data ◽  
Hand Motion ◽  
Multimodal Sensing ◽  
Sensing Technology

While the majority of social scientists still rely on traditional research instruments (e.g., surveys, self-reports, qualitative observations), multimodal sensing is becoming an emerging methodology for capturing human behaviors. Sensing technology has the potential to complement and enrich traditional measures by providing high frequency data on people’s behavior, cognition and affects. However, there is currently no easy-to-use toolkit for recording multimodal data streams. Existing methodologies rely on the use of physical sensors and custom-written code for accessing sensor data. In this paper, we present the EZ-MMLA toolkit. This toolkit was implemented as a website and provides easy access to multimodal data collection algorithms. One can collect a variety of data modalities: data on users’ attention (eye-tracking), physiological states (heart rate), body posture (skeletal data), gestures (from hand motion), emotions (from facial expressions and speech) and lower-level computer vision algorithms (e.g., fiducial/color tracking). This toolkit can run from any browser and does not require dedicated hardware or programming experience. We compare this toolkit with traditional methods and describe a case study where the EZ-MMLA toolkit was used by aspiring educational researchers in a classroom context. We conclude by discussing future work and other applications of this toolkit, potential limitations and implications.

scholarly journals ECOCAPTURE@HOME: Protocol for the Remote Assessment of Apathy and Its Everyday-Life Consequences

2021 ◽  
Vol 18 (15) ◽  
pp. 7824
Author(s):  
Valérie Godefroy ◽  
Richard Levy ◽  
Arabella Bouzigues ◽  
Armelle Rametti-Lacroux ◽  
Raffaella Migliaccio ◽  
...  
Keyword(s):  
Everyday Life ◽  
New Technologies ◽  
Electrodermal Activity ◽  
Real Life ◽  
Smartphone Application ◽  
Measurement Model ◽  
Strong Impact ◽  
Active Data ◽  
Personalised Treatment

Apathy, a common neuropsychiatric symptom associated with dementia, has a strong impact on patients’ and caregivers’ quality of life. However, it is still poorly understood and hard to define. The main objective of the ECOCAPTURE programme is to define a behavioural signature of apathy using an ecological approach. Within this program, ECOCAPTURE@HOME is an observational study which aims to validate a method based on new technologies for the remote monitoring of apathy in real life. For this study, we plan to recruit 60 couples: 20 patient-caregiver dyads in which patients suffer from behavioral variant Fronto-Temporal Dementia, 20 patient-caregiver dyads in which patients suffer from Alzheimer Disease and 20 healthy control couples. These dyads will be followed for 28 consecutive days via multi-sensor bracelets collecting passive data (acceleration, electrodermal activity, blood volume pulse). Active data will also be collected by questionnaires on a smartphone application. Using a pool of metrics extracted from these passive and active data, we will validate a measurement model for three behavioural markers of apathy (i.e., daytime activity, quality of sleep, and emotional arousal). The final purpose is to facilitate the follow-up and precise diagnosis of apathy, towards a personalised treatment of this condition within everyday life.

scholarly journals DOLARS, a Distributed On-Line Activity Recognition System by Means of Heterogeneous Sensors in Real-Life Deployments—A Case Study in the Smart Lab of The University of Almería

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 405
Author(s):  
Marcos Lupión ◽  
Javier Medina-Quero ◽  
Juan F. Sanjuan ◽  
Pilar M. Ortigosa
Keyword(s):  
Real Time ◽  
Activity Recognition ◽  
Real Life ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Heterogeneous Sensors ◽  
On Line ◽  
The University

Activity Recognition (AR) is an active research topic focused on detecting human actions and behaviours in smart environments. In this work, we present the on-line activity recognition platform DOLARS (Distributed On-line Activity Recognition System) where data from heterogeneous sensors are evaluated in real time, including binary, wearable and location sensors. Different descriptors and metrics from the heterogeneous sensor data are integrated in a common feature vector whose extraction is developed by a sliding window approach under real-time conditions. DOLARS provides a distributed architecture where: (i) stages for processing data in AR are deployed in distributed nodes, (ii) temporal cache modules compute metrics which aggregate sensor data for computing feature vectors in an efficient way; (iii) publish-subscribe models are integrated both to spread data from sensors and orchestrate the nodes (communication and replication) for computing AR and (iv) machine learning algorithms are used to classify and recognize the activities. A successful case study of daily activities recognition developed in the Smart Lab of The University of Almería (UAL) is presented in this paper. Results present an encouraging performance in recognition of sequences of activities and show the need for distributed architectures to achieve real time recognition.

scholarly journals Gas Detection and Identification Using Multimodal Artificial Intelligence Based Sensor Fusion

2021 ◽  
Vol 4 (1) ◽  
pp. 3
Author(s):  
Parag Narkhede ◽  
Rahee Walambe ◽  
Shruti Mandaokar ◽  
Pulkit Chandel ◽  
Ketan Kotecha ◽  
...  
Keyword(s):  
Sensor Fusion ◽  
Network Architecture ◽  
Cost Effective ◽  
Sensor Data ◽  
Dense Layer ◽  
Gaseous Emissions ◽  
Multiple Sensors ◽  
Single Output ◽  
Novel Approach ◽  
Single Sensor

With the rapid industrialization and technological advancements, innovative engineering technologies which are cost effective, faster and easier to implement are essential. One such area of concern is the rising number of accidents happening due to gas leaks at coal mines, chemical industries, home appliances etc. In this paper we propose a novel approach to detect and identify the gaseous emissions using the multimodal AI fusion techniques. Most of the gases and their fumes are colorless, odorless, and tasteless, thereby challenging our normal human senses. Sensing based on a single sensor may not be accurate, and sensor fusion is essential for robust and reliable detection in several real-world applications. We manually collected 6400 gas samples (1600 samples per class for four classes) using two specific sensors: the 7-semiconductor gas sensors array, and a thermal camera. The early fusion method of multimodal AI, is applied The network architecture consists of a feature extraction module for individual modality, which is then fused using a merged layer followed by a dense layer, which provides a single output for identifying the gas. We obtained the testing accuracy of 96% (for fused model) as opposed to individual model accuracies of 82% (based on Gas Sensor data using LSTM) and 93% (based on thermal images data using CNN model). Results demonstrate that the fusion of multiple sensors and modalities outperforms the outcome of a single sensor.

Multisensor Degradation Data Fusion and Remaining Life Prediction

2017 ◽  
Author(s):  
Changxi Wang ◽  
E. A. Elsayed ◽  
Kang Li ◽  
Javier Cabrera
Keyword(s):  
Data Fusion ◽  
Prediction Accuracy ◽  
Life Prediction ◽  
Clustering Algorithm ◽  
Partial Information ◽  
Degradation Process ◽  
Sensor Data ◽  
Multiple Sensors ◽  
Degradation Data ◽  
Degradation Monitoring

Multiple sensors are commonly used for degradation monitoring. Since different sensors may be sensitive at different stages of the degradation process and each sensor data contain only partial information of the degraded unit, data fusion approaches that integrate degradation data from multiple sensors can effectively improve degradation modeling and life prediction accuracy. We present a non-parametric approach that assigns weights to each sensor based on dynamic clustering of the sensors observations. A case study that involves a fatigue-crack-growth dataset is implemented in order evaluate the prognostic performance of the unit. Results show that the fused path obtained with the proposed approach outperforms any individual sensor data and other paths obtained with an adaptive threshold clustering algorithm in terms of life prediction accuracy.

scholarly journals Home-based Cardiac Rehabilitation - the real barriers of programs at distance

2021 ◽  
Vol 28 (Supplement_1) ◽  
Author(s):  
J Brito ◽  
I Aguiar-Ricardo ◽  
P Alves Da Silva ◽  
B Valente Da Silva ◽  
N Cunha ◽  
...  
Keyword(s):  
Exercise Training ◽  
Cardiac Rehabilitation ◽  
Real Time ◽  
Training Session ◽  
Real Life ◽  
Participation Rate ◽  
Exercise Session ◽  
Exterior Space ◽  
Supervised Exercise ◽  
Home Based

Abstract Funding Acknowledgements Type of funding sources: None. Introduction Despite the established benefits of cardiac rehabilitation (CR), it remains significantly underutilized. Home-based CR (CR-HB) programs should offer the same core CR components as Centre-based programs (CR-CB) but several aspects need to be adapted, communication and supervision must be improved. Although CR-HB has been successfully deployed and is a valuable alternative to CR-CB, there is less structured experience with these non-uniform programs and further studies are needed to understand which patients (pts) are indicated to this type of program. Purpose To investigate pt-perceived facilitators and barriers to home-based rehabilitation exercise. Methods Prospective cohort study which included pts who were participating in a CR-CB program and accepted to participate in a CR-HB program after CR-CB closure due to COVID-19. The CR-HB consisted in a multidisciplinary digital CR program, including pt risk evaluation and regular assessment, exercise, educational and psychological sessions. The online exercise training sessions consisted of recorded videos and real time online supervised exercise training group sessions. It was recommended to do each session 3 times per week, during 60 min. A pictorial exercise training guidebook was available to all participants including instructions regarding safety, clothing and warm-up, and a detailed illustrated description of each  exercise sessions. Also, for questions or difficulties regarding the exercises, an e-mail and telephone was provided. Once a month, real time CR exercise sessions was provided with a duration of 60min. Results 116 cardiovascular disease pts (62.6 ± 8.9years, 95 males) who were attending a face-to-face CR program were included in a CR-HB program. The majority of the pts had coronary artery disease (89%) and 5% valvular disease. Regarding risk factors, obesity was the most common (75%) followed by hypertension (60%), family history (42%), dyslipidaemia (38%), diabetes (18%), and smoking (13%). Almost half (47%) of the participants did at least one online exercise training session per week: 58% did 2-3 times per week, 27% once per week and 15% more than 4 times per week. Participants who did less than one exercise session per week reported as cause: lack of motivation (38%), preference of a different mode of exercise training such as exercise in the exterior space (26%), technology barrier such as impossibility to stream online videos (11%), fear of performing exercise without supervision (4%), and limited space at home (4%). Conclusions Our study based on real-life results of a CR-HB program shows a sub-optimal rate of participation in exercise sessions due to different causes, but mainly for the lack of motivation to exercise alone or preference for walking in exterior space. The knowledge of the CR-HB program barriers will facilitate to find out strategies to increase the participation rate and to select the best candidates for this type of programs.

Getting Enough Sleep: On the Importance of Collecting Longitudinal Data from Wearables to Assess Sleep Quality and Seasonal Effects on Variability in Daily Activity (Preprint)

2021 ◽  
Author(s):  
María Óskarsdóttir ◽  
Anna Sigridur Islind ◽  
Elias August ◽  
Erna Sif Arnardóttir ◽  
Francois Patou ◽  
...  
Keyword(s):  
Physical Activity ◽  
Heart Rate ◽  
Data Collection ◽  
Sleep Quality ◽  
Daily Activity ◽  
Hospital Environment ◽  
Sensor Data ◽  
Seasonal Patterns ◽  
Sleep Patterns ◽  
Sleep Habits

BACKGROUND The method considered the gold standard for recording sleep is a polysomnography, where the measurement is performed in a hospital environment for 1-3 nights. This requires subjects to sleep with a device and several sensors attached to their face, scalp, and body, which is both cumbersome and expensive. For longer studies with actigraphy, 3-14 days of data collection is typically used for both clinical and research studies. OBJECTIVE The primary goal of this paper is to investigate if the aforementioned timespan is sufficient for data collection, when performing sleep measurements at home using wearable and non-wearable sensors. Specifically, whether 3-14 days of data collection sufficient to capture an individual’s sleep habits and fluctuations in sleep patterns in a reliable way for research purposes. Our secondary goals are to investigate whether there is a relationship between sleep quality, physical activity, and heart rate, and whether individuals who exhibit similar activity and sleep patterns in general and in relation to seasonality can be clustered together. METHODS Data on sleep, physical activity, and heart rate was collected over a period of 6 months from 54 individuals in Denmark aged 52-86 years. The Withings Aura sleep tracker (non-wearable) and Withings Steel HR smartwatch (wearable) were used. At the individual level, we investigated the consistency of various physical activities and sleep metrics over different time spans to illustrate how sensor data from self-trackers can be used to illuminate trends. RESULTS Significant variability in standard metrics of sleep quality was found between different periods throughout the study. We show specifically that in order to get more robust individual assessment of sleep and physical activity patterns through wearable and non-wearable devices, a longer evaluation period than 3-14 days is necessary. Additionally, we found seasonal patterns in sleep data related to changing of the clock for Daylight Saving Time (DST). CONCLUSIONS We demonstrate that over two months worth of self-tracking data is needed to provide a representative summary of daily activity and sleep patterns. By doing so, we challenge the current standard of 3-14 days for sleep quality assessment and call for rethinking standards when collecting data for research purposes. Seasonal patterns and DST clock change are also important aspects that need to be taken into consideration, and designed for, when choosing a period for collecting data. Furthermore, we suggest using consumer-grade self-trackers (wearable and non-wearable ones) to support longer term evaluations of sleep and physical activity for research purposes and, possibly, clinical ones in the future.

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