A5089819897- Context-Aware Activity Recognition Systems
- Balance, Gait, and Falls Prevention
- Non-Invasive Vital Sign Monitoring
- Gait Recognition and Analysis
- Physical Activity and Health
- Healthcare Technology and Patient Monitoring
- Technology Use by Older Adults
- Mobile Health and mHealth Applications
- IoT and Edge/Fog Computing
- Advanced Sensor and Energy Harvesting Materials
- Time Series Analysis and Forecasting
- Diabetic Foot Ulcer Assessment and Management
- Obesity, Physical Activity, Diet
- Multiple Sclerosis Research Studies
- Stroke Rehabilitation and Recovery
- Robotics and Automated Systems
- Total Knee Arthroplasty Outcomes
- Chronic Obstructive Pulmonary Disease (COPD) Research
- Cerebral Palsy and Movement Disorders
- Energy Efficient Wireless Sensor Networks
- Hearing Loss and Rehabilitation
- Cardiovascular and exercise physiology
- Electronic Health Records Systems
- Bluetooth and Wireless Communication Technologies
- Millimeter-Wave Propagation and Modeling
Novartis (Switzerland)
2022
Building Engineering and Science Talent
2022
Norwegian University of Science and Technology
2015-2021
Roche (Switzerland)
2020
NTNU Samfunnsforskning
2017
Trondheim Kommune
2016
École Polytechnique Fédérale de Lausanne
2012-2015
University of Limerick
2005-2014
Ollscoil na Gaillimhe – University of Galway
2007-2011
National University of Ireland
2007
Real-world fall events objectively measured by body-worn sensors can improve the understanding of in older people. However, these are rare and hence challenging to capture. Therefore, FARSEEING (FAll Repository for design Smart sElf-adaptive Environments prolonging Independent livinG) consortium associated partners started build up a meta-database real-world falls.Between January 2012 December 2015 more than 300 have been recorded. This is currently largest collection data recorded with...
Epidemiological studies have associated the negative effects of sedentary time and patterns on health indices. However, these used methodologies that do not directly measure state. Recent technological developments in area motion sensors incorporated inclinometers, which can inclination body directly, without relying self-report or count thresholds. This paper aims to provide a detailed description examine range relevant variables, including levels from an inclinometer-based sensor. The...
A fall detection system and algorithm, incorporated into a custom designed garment has been developed. The developed uses tri-axial accelerometer to detect impacts monitor posture. This sensor is attached vest, be worn by the elderly person under clothing. algorithm was incorporates both impact posture capability. vest tested two teams of 5 subjects who wore in turn for 2 week each were monitored 8 hours day.
Adolescent females have been highlighted as a particularly sedentary population and the possible negative effects of lifestyle are being uncovered. However, much past research is based on self-report or uses indirect methods to quantity time. Total time spent intricate patterns adolescent not described using objective direct measure body inclination. The objectives this article examine levels group ActivPAL™ highlight differences in across week within school day. A full methodological...
Leveraging consumer technology such as smartphone and smartwatch devices to objectively assess people with multiple sclerosis (PwMS) remotely could capture unique aspects of disease progression. This study explores the feasibility assessing PwMS Healthy Control's (HC) physical function by characterising gaitrelated features, which can be modelled using machine learning (ML) techniques correctly distinguish subgroups from healthy controls. A total 97 subjects (24 HC subjects, 52 mildly...
This study aims to evaluate a variety of existing and novel fall detection algorithms, for waist mounted accelerometer based system. Algorithms were tested against comprehensive data-set recorded from 10 young healthy subjects performing 240 falls 120 activities daily living elderly scripted 52.4 hours continuous unscripted normal activities. Results show that using simple algorithm employing Velocity+Impact+Posture can achieve low false-positive rate less than 1 FP/day* (0.94FPs/day*) with...
Automatic fall detection will promote independent living and reduce the consequences of falls in elderly by ensuring people can confidently live safely at home for linger. In laboratory studies inertial sensor technology has been shown capable distinguishing from normal activities. However less than 7% fall-detection algorithm have used data recorded real life. The FARSEEING project compiled a database life people, to gain new knowledge about events develop algorithms combat problems...
This paper proposes a system for activity recognition using multi-sensor fusion. In this system, four sensors are attached to the waist, chest, thigh, and side of body. study we present two solutions factors that affect accuracy: calibration drift sensor orientation changing. The datasets used evaluate were collected from 8 subjects who asked perform scripted normal activities daily living (ADL), three times each. Naïve Bayes classifier fusion is adopted achieves 70.88%-97.66% accuracies 1-4 sensors.
The popularity of using wearable inertial sensors for physical activity classification has dramatically increased in the last decade due to their versatility, low form factor, and power requirements. Consequently, various systems have been developed automatically classify daily life activities. However, scope implementation such is limited laboratory-based investigations. Furthermore, these are not directly comparable, large diversity design (e.g., number sensors, placement data collection...
The measurement of gait characteristics during a self-administered 2-minute walk test (2MWT), in persons with multiple sclerosis (PwMS), using single body-worn device, has the potential to provide high-density longitudinal information on disease progression, beyond what is currently measured clinician-administered 2MWT. purpose this study determine test-retest reliability, standard error (SEM) and minimum detectable change (MDC) features calculated characteristics, harvested 2MWT home...
Falls in the elderly population are a major problem for today's society. The immediate automatic detection of such events would help reduce associated consequences falls. This paper describes development an accurate, accelerometer-based fall system to distinguish between Activities Daily Living (ADL) and It has previously been shown that falls can be distinguished from normal ADL through vertical velocity thresholding using optical motion capture system. In this study however accurate...
<ns4:p><ns4:underline>Background</ns4:underline>: Gait is a powerful tool to identify ageing and track disease progression. Yet, its high resolution measurement via traditional instruments remains restricted the laboratory or bespoke clinical facilities. The potential for that change due advances in wearables where synergy between devices smart algorithms has provided of ‘a gait lab on chip’.</ns4:p><ns4:p><ns4:underline>Methods</ns4:underline>: Commercially available quantification remain...
Physical activity monitoring algorithms are often developed using conditions that do not represent real-life activities, the target population, or labelled to a high enough resolution capture true detail of human movement. We have designed semi-structured supervised laboratory-based protocol and an unsupervised free-living recorded 20 older adults performing both protocols while wearing up 12 body-worn sensors. Subjects' movements were synchronised cameras (≥25 fps), deployed in laboratory...