ObjectivesIt remains unclear if geriatric patients with different delirium motor subtypes express... more ObjectivesIt remains unclear if geriatric patients with different delirium motor subtypes express different levels of motor activity. Thus, we used two accelerometer-based devices to simultaneously measure upright activity and wrist activity across delirium motor subtypes in geriatric patients.DesignCross-sectional study.SettingsGeriatric ward in a university hospital in Norway.ParticipantsSixty acutely admitted patients, ≥75 years, with DSM-5-delirium.Outcome measuresUpright activity measured as upright time (minutes) and sit-to-stand transitions (numbers), total wrist activity (counts) and wrist activity in a sedentary position (WAS, per cent of the sedentary time) during 24 hours ongoing Delirium Motor Subtype Scalesubtyped delirium.ResultsMean age was 86.7 years. 15 had hyperactive, 20 hypoactive, 17 mixed and 8 had no-subtype delirium. We found more upright time in the no-subtype group than in the hypoactive group (119.3 vs 37.8 min, p=0.042), but no differences between the hyp...
Objective measurement of real-world fall events by using body-worn sensor devices can improve the... more Objective measurement of real-world fall events by using body-worn sensor devices can improve the understanding of falls in older people and enable new technology to prevent, predict, and automatically recognize falls. However, relative to the required recording time, these events are rare and hence challenging to capture. Therefore, the FARSEEING (FAll Repository for the design of Smart and sElf-adapaive Environments prolonging INdependent livinG) consortium and associated partners established a meta-database of signals from real-world falls. Until the end of 2014, 397 falls were measured and reported. This includes falls data from several settings and disease groups, mainly geriatric rehabilitation, Parkinson's disease, cerebellar and sensory ataxia. Seventy-five per cent of the falls were measured with a sampling rate of 100 Hz with devices including at least accelerometers and gyroscopes. To date more than 100 of these real-world falls have been validated and finally processed for data analyses. The observed signal patterns showed a high heterogeneity and differed considerably from those of simulated falls. Preliminary analyses of the available real-world falls data with two different fall-detection approaches using wavelets as well as temporal and mechanical thresholds considerably improved the detection performance. The FARSEEING consortium will continue to increase the number of measured real-world falls in the meta-database beyond the end of the project. External users can request data access on the FARSEEING website.
... Pepijn van de Ven Department of Electronic & Computer Engineering University of Limerick ... more ... Pepijn van de Ven Department of Electronic & Computer Engineering University of Limerick [email protected] ... Such impacts are the first trigger for a fall analysis, as displayed in figure 3. Upon measuring an acceleration exceeding the thresh-old, an algorithm is ...
Gait is a powerful tool to identify ageing and track disease Background progression. Yet, its hig... more Gait is a powerful tool to identify ageing and track disease Background progression. Yet, its high resolution measurement via traditional instruments remains restricted to the laboratory or bespoke clinical facilities. The potential for that to change is due to the advances in wearables where the synergy between devices and smart algorithms has provided the potential of 'a gait lab on a chip'. : Commercially available wearables for gait quantification remain Methods expensive and are restricted to a limited number of characteristics unsuitable for a comprehensive assessment required within intervention or epidemiological studies. However, the increasing demand for low-cost diagnostics has fuelled the shift in how health-related resources are distributed. As such we adopt open platform technology and validated research methodologies to harmonise engineering solutions to satisfy current epidemiological needs. : We provide an introduction to conduct a routine instrumented gait Results assessment with a discrete, low-cost, accelerometer-based wearable. We show that the capture and interpretation of raw gait signals with a common scripting language can be straightforward and suitable for use within modern studies. We highlight the best approaches and hope that this will help compliment any analytical tool-kit as part of future cohort assessments. : Deployment of wearables can allow accurate gait assessment Conclusions in accordance with advocated methods of data collection as there is a strong demand for sensitive outcomes derived from pragmatic tools. This tutorial shows that instrumentation of gait using a single open source wearable is pragmatic due to low-cost and translational analytical methods to derive sensitive outcomes.
Advances in wearable/mobile device technologies make possible long-term recording of data in our ... more Advances in wearable/mobile device technologies make possible long-term recording of data in our everyday life contexts. Of particular interest is availability of inertial sensors allowing to monitor daily physical activity behavior, which is thought to include useful information on physiology, age/disease related functional capacity, and quality of life. The challenging task in this interdisciplinary research context is to translate the raw data into interpretable information and knowledge that can be further exploited to provide valid hypothesis, objective evaluation and diagnosis. The aim of this paper is to present a methodological framework that brings together monitoring technology, mathematical tools and modern clinical concepts of physiological complexity, with the aim to reveal and quantify aspects of age-/health-related physical behavior embedded in patterns of everyday life activity.
Increased levels of light, moderate and vigorous physical activity (PA) are positively associated... more Increased levels of light, moderate and vigorous physical activity (PA) are positively associated with health benefits. Therefore, sensor-based human activity recognition can identify different types and levels of PA. In this paper, we propose a two-layer locomotion recognition method using dynamic time warping applied to inertial sensor data. Based on a video-validated dataset (ADAPT), which included inertial sensor data recorded at the lower back (L5 position) during an unsupervised task-based free-living protocol, the recognition algorithm was developed, validated and tested. As a first step, we focused on the identification of locomotion activities walking, ascending and descending stairs. These activities are difficult to differentiate due to a high similarity. The results showed that walking could be recognized with a sensitivity of 88% and a specificity of 89%. Specificity for stair climbing was higher compared to walking, but sensitivity was noticeably decreased. In most cas...
Journal for the Measurement of Physical Behaviour, 2019
Background: This study aims to perform a concurrent criterion validation of the activPAL3 activit... more Background: This study aims to perform a concurrent criterion validation of the activPAL3 activity monitor, in the detection of physical activity, steps, and postural transfers in older adults usin...
Please cite this article in press as: Bourke AK, et al. Development of a gold-standard method for... more Please cite this article in press as: Bourke AK, et al. Development of a gold-standard method for the identification of sedentary, light and moderate physical activities in older adults: Definitions for video annotation.
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, Aug 1, 2016
We have validated a real-time activity classification algorithm based on monitoring by a body wor... more We have validated a real-time activity classification algorithm based on monitoring by a body worn system which is potentially suitable for low-power applications on a relatively computationally lightweight processing unit. The algorithm output was validated using annotation data generated from video recordings of 20 elderly volunteers performing both a semi-structured protocol and a free-living protocol. The algorithm correctly identified sitting 75.1% of the time, standing 68.8% of the time, lying 50.9% of the time, and walking and other upright locomotion 82.7% of the time. This is one of the most detailed validations of a body worn sensor algorithm to date and offers an insight into the challenges of developing a real-time physical activity classification algorithm for a tri-axial accelerometer based sensor worn at the waist.
The popularity of using wearable inertial sensors for physical activity classification has dramat... more 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 low power requirements. Consequently, various systems have been developed to automatically classify daily life activities. However, the scope and implementation of such systems is limited to laboratory-based investigations. Furthermore, these systems are not directly comparable, due to the large diversity in their design (e.g., number of sensors, placement of sensors, data collection environments, data processing techniques, features set, classifiers, cross-validation methods). Hence, the aim of this study is to propose a fair and unbiased benchmark for the field-based validation of three existing systems, highlighting the gap between laboratory and real-life conditions. For this purpose, three representative state-of-the-art systems are chosen and implemented to classify the physical activities of twenty ...
Home Control and Automation systems are often modular and offer the flexibility and dependability... more Home Control and Automation systems are often modular and offer the flexibility and dependability to make life easier. Wearable sensor systems for health monitoring are an emerging trend and are expected to enable proactive personal health management. Using home-based technology and personal devices the aim is to motivate and support healthier lifestyle; this is a challenge which has been addressed in the framework of FARSEEING and CuPiD EU projects. Contrary to visions that consider home automation and personal health systems as a mean to replace or to simplify the subject control and actions, in the FARSEEING and CuPiD approach smartphones, wearable devices, and home based technology are used to stimulate the user by making life mentally and physically more challenging but without losing comfort.
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, Aug 1, 2016
Automatic fall detection will promote independent living and reduce the consequences of falls in ... more Automatic fall detection will promote independent living and reduce the consequences of falls in the elderly by ensuring people can confidently live safely at home for linger. In laboratory studies inertial sensor technology has been shown capable of distinguishing falls from normal activities. However less than 7% of fall-detection algorithm studies have used fall data recorded from elderly people in real life. The FARSEEING project has compiled a database of real life falls from elderly people, to gain new knowledge about fall events and to develop fall detection algorithms to combat the problems associated with falls. We have extracted 12 different kinematic, temporal and kinetic related features from a data-set of 89 real-world falls and 368 activities of daily living. Using the extracted features we applied machine learning techniques and produced a selection of algorithms based on different feature combinations. The best algorithm employs 10 different features and produced a s...
ObjectivesIt remains unclear if geriatric patients with different delirium motor subtypes express... more ObjectivesIt remains unclear if geriatric patients with different delirium motor subtypes express different levels of motor activity. Thus, we used two accelerometer-based devices to simultaneously measure upright activity and wrist activity across delirium motor subtypes in geriatric patients.DesignCross-sectional study.SettingsGeriatric ward in a university hospital in Norway.ParticipantsSixty acutely admitted patients, ≥75 years, with DSM-5-delirium.Outcome measuresUpright activity measured as upright time (minutes) and sit-to-stand transitions (numbers), total wrist activity (counts) and wrist activity in a sedentary position (WAS, per cent of the sedentary time) during 24 hours ongoing Delirium Motor Subtype Scalesubtyped delirium.ResultsMean age was 86.7 years. 15 had hyperactive, 20 hypoactive, 17 mixed and 8 had no-subtype delirium. We found more upright time in the no-subtype group than in the hypoactive group (119.3 vs 37.8 min, p=0.042), but no differences between the hyp...
Objective measurement of real-world fall events by using body-worn sensor devices can improve the... more Objective measurement of real-world fall events by using body-worn sensor devices can improve the understanding of falls in older people and enable new technology to prevent, predict, and automatically recognize falls. However, relative to the required recording time, these events are rare and hence challenging to capture. Therefore, the FARSEEING (FAll Repository for the design of Smart and sElf-adapaive Environments prolonging INdependent livinG) consortium and associated partners established a meta-database of signals from real-world falls. Until the end of 2014, 397 falls were measured and reported. This includes falls data from several settings and disease groups, mainly geriatric rehabilitation, Parkinson's disease, cerebellar and sensory ataxia. Seventy-five per cent of the falls were measured with a sampling rate of 100 Hz with devices including at least accelerometers and gyroscopes. To date more than 100 of these real-world falls have been validated and finally processed for data analyses. The observed signal patterns showed a high heterogeneity and differed considerably from those of simulated falls. Preliminary analyses of the available real-world falls data with two different fall-detection approaches using wavelets as well as temporal and mechanical thresholds considerably improved the detection performance. The FARSEEING consortium will continue to increase the number of measured real-world falls in the meta-database beyond the end of the project. External users can request data access on the FARSEEING website.
... Pepijn van de Ven Department of Electronic & Computer Engineering University of Limerick ... more ... Pepijn van de Ven Department of Electronic & Computer Engineering University of Limerick [email protected] ... Such impacts are the first trigger for a fall analysis, as displayed in figure 3. Upon measuring an acceleration exceeding the thresh-old, an algorithm is ...
Gait is a powerful tool to identify ageing and track disease Background progression. Yet, its hig... more Gait is a powerful tool to identify ageing and track disease Background progression. Yet, its high resolution measurement via traditional instruments remains restricted to the laboratory or bespoke clinical facilities. The potential for that to change is due to the advances in wearables where the synergy between devices and smart algorithms has provided the potential of 'a gait lab on a chip'. : Commercially available wearables for gait quantification remain Methods expensive and are restricted to a limited number of characteristics unsuitable for a comprehensive assessment required within intervention or epidemiological studies. However, the increasing demand for low-cost diagnostics has fuelled the shift in how health-related resources are distributed. As such we adopt open platform technology and validated research methodologies to harmonise engineering solutions to satisfy current epidemiological needs. : We provide an introduction to conduct a routine instrumented gait Results assessment with a discrete, low-cost, accelerometer-based wearable. We show that the capture and interpretation of raw gait signals with a common scripting language can be straightforward and suitable for use within modern studies. We highlight the best approaches and hope that this will help compliment any analytical tool-kit as part of future cohort assessments. : Deployment of wearables can allow accurate gait assessment Conclusions in accordance with advocated methods of data collection as there is a strong demand for sensitive outcomes derived from pragmatic tools. This tutorial shows that instrumentation of gait using a single open source wearable is pragmatic due to low-cost and translational analytical methods to derive sensitive outcomes.
Advances in wearable/mobile device technologies make possible long-term recording of data in our ... more Advances in wearable/mobile device technologies make possible long-term recording of data in our everyday life contexts. Of particular interest is availability of inertial sensors allowing to monitor daily physical activity behavior, which is thought to include useful information on physiology, age/disease related functional capacity, and quality of life. The challenging task in this interdisciplinary research context is to translate the raw data into interpretable information and knowledge that can be further exploited to provide valid hypothesis, objective evaluation and diagnosis. The aim of this paper is to present a methodological framework that brings together monitoring technology, mathematical tools and modern clinical concepts of physiological complexity, with the aim to reveal and quantify aspects of age-/health-related physical behavior embedded in patterns of everyday life activity.
Increased levels of light, moderate and vigorous physical activity (PA) are positively associated... more Increased levels of light, moderate and vigorous physical activity (PA) are positively associated with health benefits. Therefore, sensor-based human activity recognition can identify different types and levels of PA. In this paper, we propose a two-layer locomotion recognition method using dynamic time warping applied to inertial sensor data. Based on a video-validated dataset (ADAPT), which included inertial sensor data recorded at the lower back (L5 position) during an unsupervised task-based free-living protocol, the recognition algorithm was developed, validated and tested. As a first step, we focused on the identification of locomotion activities walking, ascending and descending stairs. These activities are difficult to differentiate due to a high similarity. The results showed that walking could be recognized with a sensitivity of 88% and a specificity of 89%. Specificity for stair climbing was higher compared to walking, but sensitivity was noticeably decreased. In most cas...
Journal for the Measurement of Physical Behaviour, 2019
Background: This study aims to perform a concurrent criterion validation of the activPAL3 activit... more Background: This study aims to perform a concurrent criterion validation of the activPAL3 activity monitor, in the detection of physical activity, steps, and postural transfers in older adults usin...
Please cite this article in press as: Bourke AK, et al. Development of a gold-standard method for... more Please cite this article in press as: Bourke AK, et al. Development of a gold-standard method for the identification of sedentary, light and moderate physical activities in older adults: Definitions for video annotation.
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, Aug 1, 2016
We have validated a real-time activity classification algorithm based on monitoring by a body wor... more We have validated a real-time activity classification algorithm based on monitoring by a body worn system which is potentially suitable for low-power applications on a relatively computationally lightweight processing unit. The algorithm output was validated using annotation data generated from video recordings of 20 elderly volunteers performing both a semi-structured protocol and a free-living protocol. The algorithm correctly identified sitting 75.1% of the time, standing 68.8% of the time, lying 50.9% of the time, and walking and other upright locomotion 82.7% of the time. This is one of the most detailed validations of a body worn sensor algorithm to date and offers an insight into the challenges of developing a real-time physical activity classification algorithm for a tri-axial accelerometer based sensor worn at the waist.
The popularity of using wearable inertial sensors for physical activity classification has dramat... more 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 low power requirements. Consequently, various systems have been developed to automatically classify daily life activities. However, the scope and implementation of such systems is limited to laboratory-based investigations. Furthermore, these systems are not directly comparable, due to the large diversity in their design (e.g., number of sensors, placement of sensors, data collection environments, data processing techniques, features set, classifiers, cross-validation methods). Hence, the aim of this study is to propose a fair and unbiased benchmark for the field-based validation of three existing systems, highlighting the gap between laboratory and real-life conditions. For this purpose, three representative state-of-the-art systems are chosen and implemented to classify the physical activities of twenty ...
Home Control and Automation systems are often modular and offer the flexibility and dependability... more Home Control and Automation systems are often modular and offer the flexibility and dependability to make life easier. Wearable sensor systems for health monitoring are an emerging trend and are expected to enable proactive personal health management. Using home-based technology and personal devices the aim is to motivate and support healthier lifestyle; this is a challenge which has been addressed in the framework of FARSEEING and CuPiD EU projects. Contrary to visions that consider home automation and personal health systems as a mean to replace or to simplify the subject control and actions, in the FARSEEING and CuPiD approach smartphones, wearable devices, and home based technology are used to stimulate the user by making life mentally and physically more challenging but without losing comfort.
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, Aug 1, 2016
Automatic fall detection will promote independent living and reduce the consequences of falls in ... more Automatic fall detection will promote independent living and reduce the consequences of falls in the elderly by ensuring people can confidently live safely at home for linger. In laboratory studies inertial sensor technology has been shown capable of distinguishing falls from normal activities. However less than 7% of fall-detection algorithm studies have used fall data recorded from elderly people in real life. The FARSEEING project has compiled a database of real life falls from elderly people, to gain new knowledge about fall events and to develop fall detection algorithms to combat the problems associated with falls. We have extracted 12 different kinematic, temporal and kinetic related features from a data-set of 89 real-world falls and 368 activities of daily living. Using the extracted features we applied machine learning techniques and produced a selection of algorithms based on different feature combinations. The best algorithm employs 10 different features and produced a s...
On long distance journeys passengers at high risk from deep vein thrombosis (DVT) are recommended... more On long distance journeys passengers at high risk from deep vein thrombosis (DVT) are recommended to exercise on a regular basis to contract the calf muscle pump and encourage venous return. If a passenger fails to complete an exercise program that induces active contraction of the calf muscle pump they will remain at increased risk of DVT. This paper presents a novel inertial and magnetic sensor-based technique for monitoring calf muscle pump activity. The technique could be implemented into a system for monitoring the level of calf muscle pump activity in persons with limited mobility. Such a system could be used to provide a reminder to the user that there is a need to exercise should they have forgotten to exercise, failed to exercise sufficiently or exercised incorrectly. The proposed technique was evaluated by comparison with calf muscle pump activity measured using an electromyography (EMG) sensor. Results show that the technique can be used to monitor calf muscle pump activity over a wide range of leg exercises.
This study investigates distinguishing falls from normal Activities of Daily Living (ADL) by thre... more This study investigates distinguishing falls from normal Activities of Daily Living (ADL) by thresholding of the vertical velocity of the trunk. Also presented is the design and evaluation of a wearable inertial sensor, capable of accurately measuring these vertical velocity profiles, thus providing an alternative to optical motion capture systems. Five young healthy subjects performed a number of simulated falls and normal ADL and their trunk vertical velocities were measured by both the optical motion capture system and the inertial sensor. Through vertical velocity thresholding (VVT) of the trunk, obtained from the optical motion capture system, at −1.3 m/s, falls can be distinguished from normal ADL, with 100% accuracy and with an average of 323 ms prior to trunk impact and 140 ms prior to knee impact, in this subject group. The vertical velocity profiles obtained using the inertial sensor, were then compared to those obtained using the optical motion capture system. The signals from the inertial sensor were combined to produce vertical velocity profiles using rotational mathematics and integration. Results show high mean correlation (0.941: Coefficient of Multiple Correlations) and low mean percentage error (6.74%) between the signals generated from the inertial sensor to those from the optical motion capture system. The proposed system enables vertical velocity profiles to be measured from elderly subjects in a home environment where as this has previously been impractical.
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Papers by Alan Bourke