document

Power in wheelchair propulsion: Advancing the use of wearable sensors for wheelchair sports practice

van Dijk, M.P. (author)

From the perspective of wheelchair sports practice, there is a growing need to provide feedback on power output. Power can be used to monitor athletes' fitness and fatigue levels, thereby preventing overtraining and potential injuries, to individualize and analyze training programs, and it is also an objective measure of effort or intensity....

doctoral thesis 2024

document

Observing human gait and detecting falls: A model-based approach based on wearable sensors

Umans, Sachin (author)

Falling remains a large source of (traumatic) injuries and healthcare costs. Over the past years, different actuators have been developed in the field of wearable robotics to help prevent injuries from falling. To increase the wearability of these systems, the weight of power storage can be decreased with intermittent instead of continuous...

master thesis 2024

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Quantifying Biomedical Amplifier Efficiency: The noise efficiency factor

Hall, Drew A. (author), Makinwa, K.A.A. (author), Jang, Taekwang (author)

There has been a long-standing interest in controlling and instrumenting the human body. Whether to restore lost function with neural prosthetics, monitor blood glucose levels, or augment human capabilities, there are countless opportunities for sensors inside ( e.g., ingestible, injectable, and implantable) and outside ( e.g., wearable) the...

journal article 2023

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Real-Life Wheelchair Mobility Metrics from IMUs

de Vries, W.H.K. (author), van der Slikke, R.M.A. (author), van Dijk, M.P. (author), Arnet, Ursina (author)

Daily wheelchair ambulation is seen as a risk factor for shoulder problems, which are prevalent in manual wheelchair users. To examine the long-term effect of shoulder load from daily wheelchair ambulation on shoulder problems, quantification is required in real-life settings. In this study, we describe and validate a comprehensive and...

journal article 2023

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Improving state estimation through projection post-processing for activity recognition with application to football

Ciszewski, M.G. (author), Söhl, J. (author), Jongbloed, G. (author)

The past decade has seen an increased interest in human activity recognition based on sensor data. Most often, the sensor data come unannotated, creating the need for fast labelling methods. For assessing the quality of the labelling, an appropriate performance measure has to be chosen. Our main contribution is a novel post-processing method...

journal article 2023

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Using Wearable Sensors to Estimate Mechanical Power Output in Cyclical Sports Other than Cycling: A Review

De Vette, V.G. (author), Veeger, H.E.J. (author), van Dijk, M.P. (author)

More insight into in-field mechanical power in cyclical sports is useful for coaches, sport scientists, and athletes for various reasons. To estimate in-field mechanical power, the use of wearable sensors can be a convenient solution. However, as many model options and approaches for mechanical power estimation using wearable sensors exist, and...

review 2022

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Wearable airbag technology and machine learned models to mitigate falls after stroke

Botonis, Olivia K. (author), Harari, Yaar (author), Embry, Kyle R. (author), Mummidisetty, Chaithanya K. (author), Riopelle, David (author), Giffhorn, Matt (author), Albert, Mark V. (author), Vallery, H. (author), Jayaraman, Arun (author)

Background: Falls are a common complication experienced after a stroke and can cause serious detriments to physical health and social mobility, necessitating a dire need for intervention. Among recent advancements, wearable airbag technology has been designed to detect and mitigate fall impact. However, these devices have not been designed...

journal article 2022

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Classification of Wheelchair Related Shoulder Loading Activities from Wearable Sensor Data: A Machine Learning Approach

de Vries, Wiebe H.K. (author), Amrein, Sabrina (author), Arnet, Ursina (author), Mayrhuber, Laura (author), Ehrmann, Cristina (author), Veeger, H.E.J. (author)

Shoulder problems (pain and pathology) are highly prevalent in manual wheelchair users with spinal cord injury. These problems lead to limitations in activities of daily life (ADL), labor- and leisure participation, and increase the health care costs. Shoulder problems are often associated with the long-term reliance on the upper limbs, and...

journal article 2022

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Wearable Sensor-Based Real-Time Gait Detection: A Systematic Review

Prasanth, H. (author), Caban, Miroslav (author), Keller, Urs (author), Courtine, Grégoire (author), Ijspeert, Auke J. (author), Vallery, H. (author), Von Zitzewitz, Joachim (author)

Gait analysis has traditionally been carried out in a laboratory environment using expensive equipment, but, recently, reliable, affordable, and wearable sensors have enabled integration into clinical applications as well as use during activities of daily living. Real-time gait analysis is key to the development of gait rehabilitation techniques...

journal article 2021

document

Smart sensor tights: Movement tracking of the lower limbs in football

Steijlen, A.S.M. (author), Burgers, D.B.J. (author), Wilmes, Erik (author), Bastemeijer, J. (author), Bastiaansen, Bram J.C. (author), French, P.J. (author), Bossche, A. (author), Jansen, K.M.B. (author)

This article presents a novel smart sensor garment with integrated miniaturized inertial measurements units (IMUs) that can be used to monitor lower body kinematics during daily training activities, without the need of extensive technical assistance throughout the measurements. The smart sensor tights enclose five ultra-light sensor modules that...

journal article 2021

document

The power of ECG in multimodal patient-specific seizure monitoring: Added value to an EEG-based detector using limited channels

Vandecasteele, Kaat (author), De Cooman, Thomas (author), Chatzichristos, Christos (author), Cleeren, Evy (author), Swinnen, Lauren (author), Ortiz, Jaiver Macea (author), Van Huffel, Sabine (author), Dümpelmann, Matthias (author), Schulze- Bonhage, Andreas (author), De Vos, Maarten (author), Van Paesschen, Wim (author), Hunyadi, Borbala (author)

Objective: Wearable seizure detection devices could provide more reliable seizure documentation outside the hospital compared to seizure self-reporting by patients, which is the current standard. Previously, during the SeizeIT1 project, we studied seizure detection based on behind-the-ear electroencephalography (EEG). However, the obtained...

journal article 2021

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Development of Sensor Tights with Integrated Inertial Measurement Units for Injury Prevention in Football

Steijlen, A.S.M. (author), Bastemeijer, J. (author), Plaude, L. (author), French, P.J. (author), Bossche, A. (author), Jansen, K.M.B. (author)

In elite European football, 6 to 7 hamstring muscle injuries occur per team per season, which results in an absence of 14 to 180 days (Ekstrand et al. 2017). These injuries occur typically in the last part of a training or match. This implies that the accumulation of demanding actions is an important factor for hamstring injury risk. In current...

conference paper 2020

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Wearable monitoring and interpretable machine learning can objectively track progression in patients during cardiac rehabilitation

De Cannière, Hélène (author), Corradi, Federico (author), Smeets, Christophe J.P. (author), Schoutteten, Melanie (author), Varon, Carolina (author), Van Hoof, Chris (author), Van Huffel, Sabine (author), Groenendaal, Willemijn (author), Vandervoort, Pieter (author)

Cardiovascular diseases (CVD) are often characterized by their multifactorial complexity. This makes remote monitoring and ambulatory cardiac rehabilitation (CR) therapy challenging. Current wearable multimodal devices enable remote monitoring. Machine learning (ML) and artificial intelligence (AI) can help in tackling multifaceted datasets....

journal article 2020

document

Visual seizure annotation and automated seizure detection using behind-the-ear electroencephalographic channels

Vandecasteele, Kaat (author), De Cooman, Thomas (author), Dan, Jonathan (author), Cleeren, Evy (author), Van Huffel, Sabine (author), Hunyadi, Borbala (author), Van Paesschen, Wim (author)

Objective: Seizure diaries kept by patients are unreliable. Automated electroencephalography (EEG)-based seizure detection systems are a useful support tool to objectively detect and register seizures during long-term video-EEG recording. However, this standard full scalp-EEG recording setup is of limited use outside the hospital, and a...

journal article 2020

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Drift-Free Inertial Sensor-Based Joint Kinematics for Long-Term Arbitrary Movements

Weygers, Ive (author), Kok, M. (author), De Vroey, Henri (author), Verbeerst, Tommy (author), Versteyhe, Mark (author), Hallez, Hans (author), Claeys, Kurt (author)

The ability to capture joint kinematics in outside-laboratory environments is clinically relevant. In order to estimate kinematics, inertial measurement units can be attached to body segments and their absolute orientations can be estimated. However, the heading part of such orientation estimates is known to drift over time, resulting in...

journal article 2020

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Multimodal data collection for social interaction analysis in-the-wild

Hung, H.S. (author), Raman, C.A. (author), Gedik, E. (author), Tan, S. (author), Vargas Quiros, J.D. (author)

The benefits of exploiting multi-modality in the analysis of human-human social behaviour has been demonstrated widely in the community. An important aspect of this problem is the collection of data-sets that provide a rich and realistic representation of how people actually socialize with each other in real life. These subtle coordination...

conference paper 2019

document

A Hierarchical Approach for Associating Body-Worn Sensors to Video Regions in Crowded Mingling Scenarios

Cabrera Quiros, L.C. (author), Hung, H.S. (author)

We address the complex problem of associating several wearable devices with the spatio-temporal region of their wearers in video during crowded mingling events using only acceleration and proximity. This is a particularly important first step for multi-sensor behavior analysis using video and wearable technologies, where the privacy of the...

journal article 2019

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Augmenting Clinical Outcome Measures of Gait and Balance with a Single Inertial Sensor in Age-Ranged Healthy Adults

O'Brien, Megan K. (author), Hidalgo-Araya, Marco D. (author), Mummidisetty, Chaithanya K. (author), Vallery, H. (author), Ghaffari, Roozbeh (author), Rogers, John A. (author), Lieber, Richard (author), Jayaraman, Arun (author)

Gait and balance impairments are linked with reduced mobility and increased risk of falling. Wearable sensing technologies, such as inertial measurement units (IMUs), may augment clinical assessments by providing continuous, high-resolution data. This study tested and validated the utility of a single IMU to quantify gait and balance features...

journal article 2019

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Towards Analyzing and Predicting the Experience of Live Performances with Wearable Sensing

Gedik, E. (author), Cabrera Quiros, L.C. (author), Martella, Claudio (author), Englebienne, Gwenn (author), Hung, H.S. (author)

We present an approach to interpret the response of audiences to live performances by processing mobile sensor data. We apply our method on three different datasets obtained from three live performances, where each audience member wore a single tri-axial accelerometer and proximity sensor embedded inside a smart sensor pack. Using these...

journal article 2018

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Personalised models for speech detection from body movements using transductive parameter transfer

Gedik, E. (author), Hung, H.S. (author)

We investigate the task of detecting speakers in crowded environments using a single body worn triaxial accelerometer. Detection of such behaviour is very challenging to model as people’s body movements during speech vary greatly. Similar to previous studies, by assuming that body movements are indicative of speech, we show experimentally, on...

journal article 2017