Quantification of the relative arm use in patients with hemiparesis using inertial measurement units.
07 Jul 2021-Vol. 8, pp 20556683211019694
TL;DR: In this article, an inertial measurement unit (IMU)-based girder was used for activity counting for measuring arm use, which is prone to overestimation due to non-functional movements.
Abstract: IntroductionAccelerometry-based activity counting for measuring arm use is prone to overestimation due to non-functional movements. In this paper, we used an inertial measurement unit (IMU)-based g...
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TL;DR: In this paper, the authors present a framework for assessing upper-limb functioning using sensors by providing: (a) a set of definitions of important constructs associated with upper limb functioning; (b) different visualization methods for evaluating upper limb function; and (c) two new measures for quantifying how much an upper limb is used and the relative bias in their use.
Abstract: The ultimate goal of any upper-limb neurorehabilitation procedure is to improve upper-limb functioning in daily life. While clinic-based assessments provide an assessment of what a patient can do, they do not completely reflect what a patient does in his/her daily life. The use of compensatory strategies such as the use of the less affected upper-limb or excessive use of trunk in daily life is a common behavioral pattern seen in patients with hemiparesis. To this end, there has been an increasing interest in the use of wearable sensors to objectively assess upper-limb functioning. This paper presents a framework for assessing upper-limb functioning using sensors by providing: (a) a set of definitions of important constructs associated with upper-limb functioning; (b) different visualization methods for evaluating upper-limb functioning; and (c) two new measures for quantifying how much an upper-limb is used and the relative bias in their use. The demonstration of some of these components is presented using data collected from inertial measurement units from a previous study. The proposed framework can help guide the future technical and clinical work in this area to realize valid, objective, and robust tools for assessing upper-limb functioning. This will in turn drive the refinement and standardization of the assessment of upper-limb functioning.
9 citations
TL;DR: It is concluded that sensor-based measures of movement provide additional information in relation to clinical evaluation tools assessing motor functioning and both are needed to gain better insight in patient behavior and recovery.
Abstract: Stroke is a main cause of long-term disability worldwide, placing a large burden on individuals and health care systems. Wearable technology can potentially objectively assess and monitor patients outside clinical environments, enabling a more detailed evaluation of their impairment and allowing individualization of rehabilitation therapies. The aim of this review is to provide an overview of setups used in literature to measure movement of stroke patients under free living conditions using wearable sensors, and to evaluate the relation between such sensor-based outcomes and the level of functioning as assessed by existing clinical evaluation methods. After a systematic search we included 32 articles, totaling 1076 stroke patients from acute to chronic phases and 236 healthy controls. We summarized the results by type and location of sensors, and by sensor-based outcome measures and their relation with existing clinical evaluation tools. We conclude that sensor-based measures of movement provide additional information in relation to clinical evaluation tools assessing motor functioning and both are needed to gain better insight in patient behavior and recovery. However, there is a strong need for standardization and consensus, regarding clinical assessments, but also regarding the use of specific algorithms and metrics for unsupervised measurements during daily life.
7 citations
TL;DR: In this paper , an activity measurement based on an acceleration sensor integrated into a smartwatch was used to evaluate the performance of complex upper extremity-based manual activities of daily living (ADL) tasks in frail elderly.
Abstract: Frailty is accompanied by limitations of activities of daily living (ADL) and frequently associated with reduced quality of life, institutionalization, and higher health care costs. Despite the importance of ADL performance for the consequence of frailty, movement analyses based on kinematic markers during the performance of complex upper extremity-based manual ADL tasks in frail elderly is still pending. The main objective of this study was to evaluate if ADL task performance of two different tasks in frail elderlies can be assessed by an activity measurement based on an acceleration sensor integrated into a smartwatch, and further to what degree kinematic parameters would be task independent.ADL data was obtained from twenty-seven elderly participants (mean age 81.6 ± 7.0 years) who performed two ADL tasks. Acceleration data of the dominant hand was collected using a smartwatch. Participants were split up in three groups, F (frail, n = 6), P (pre-frail, n = 13) and R (robust, n = 8) according to a frailty screening. A variety of kinematic measures were calculated from the vector product reflecting activity, agility, smoothness, energy, and intensity.Measures of agility, smoothness, and intensity revealed significant differences between the groups (effect sizes combined over tasks η2p = 0.18 - 0.26). Smoothness was particularly affected by frailty in the tea making task, while activity, agility, a different smoothness parameter and two intensity measures were related to frailty in the gardening task. Four of nine parameters revealed good reliability over both tasks (r = 0.44 - 0.69). Multiple linear regression for the data combined across tasks showed that only the variability of the magnitude of acceleration peaks (agility) contributed to the prediction of the frailty score (R2 = 0.25).The results demonstrate that ADL task performance can be assessed by smartwatch-based measures and further shows task-independent differences between the three levels of frailty. From the pattern of impaired and preserved performance parameters across the tested tasks, we concluded that in persons with frailty ADL performance was more impaired by physiological deficiencies, i.e., physical power and endurance, than by cognitive functioning or sensorimotor control.
3 citations
TL;DR: Intra-subject random forest machine learning measures were found to classify upper limb use more accurately than other measures and use information about the orientation and the amount of movement of the forearm to detectupper limb use.
Abstract: The various existing measures to quantify upper limb use from wrist-worn inertial measurement units (IMU) can be grouped into three categories: (a) Thresholded activity counting, (b) Gross movement score and (c) machine learning. While machine learning algorithms are a promising approach to detect upper limb use, there is currently no knowledge of the information used by these methods, and the data-related factors that influence their performance. A comparison of existing methods was carried out using data from a previous study which was collected from 10 unimpaired and 5 hemiparetic subjects, with annotation to identify periods of functional and non-functional upper limb use. Intra-subject random forest machine learning measures were found to classify upper limb use more accurately than other measures. The random forest measure uses information about the orientation and the amount of movement of the forearm to detect upper limb use. The types of movements and the proportion of functional data included in training/testing set influences the performance of machine learning measures. This study presents the first step towards understanding and optimizing machine learning methods for upper limb use assessment using wearable sensors.
2 citations
TL;DR: A machine learning classifier is developed and validated for this task and compared it to methods using conventional and optimal thresholds to compare the validity of methods classifying stroke survivors’ real-life arm activities measured by wrist-worn sensors excluding whole-body movements.
Abstract: Background: Arm use metrics derived from wrist-mounted movement sensors are widely used to quantify the upper limb performance in real-life conditions of individuals with stroke throughout motor recovery. The calculation of real-world use metrics, such as arm use duration and laterality preferences, relies on accurately identifying functional movements. Hence, classifying upper limb activity into functional and non-functional classes is paramount. Acceleration thresholds are conventionally used to distinguish these classes. However, these methods are challenged by the high inter and intra-individual variability of movement patterns. In this study, we developed and validated a machine learning classifier for this task and compared it to methods using conventional and optimal thresholds. Methods: Individuals after stroke were video-recorded in their home environment performing semi-naturalistic daily tasks while wearing wrist-mounted inertial measurement units. Data were labeled frame-by-frame following the Taxonomy of Functional Upper Limb Motion definitions, excluding whole-body movements, and sequenced into 1-s epochs. Actigraph counts were computed, and an optimal threshold for functional movement was determined by receiver operating characteristic curve analyses on group and individual levels. A logistic regression classifier was trained on the same labels using time and frequency domain features. Performance measures were compared between all classification methods. Results: Video data (6.5 h) of 14 individuals with mild-to-severe upper limb impairment were labeled. Optimal activity count thresholds were ≥20.1 for the affected side and ≥38.6 for the unaffected side and showed high predictive power with an area under the curve (95% CI) of 0.88 (0.87,0.89) and 0.86 (0.85, 0.87), respectively. A classification accuracy of around 80% was equivalent to the optimal threshold and machine learning methods and outperformed the conventional threshold by ∼10%. Optimal thresholds and machine learning methods showed superior specificity (75–82%) to conventional thresholds (58–66%) across unilateral and bilateral activities. Conclusion: This work compares the validity of methods classifying stroke survivors’ real-life arm activities measured by wrist-worn sensors excluding whole-body movements. The determined optimal thresholds and machine learning classifiers achieved an equivalent accuracy and higher specificity than conventional thresholds. Our open-sourced classifier or optimal thresholds should be used to specify the intensity and duration of arm use.
2 citations
References
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TL;DR: Accelerometer-based activity assessments requires careful planning and the use of appropriate strategies to increase compliance, and face-to-face distribution and collection of accelerometers is probably the best option in field-based research, but deliveries by express carrier or registered mail is a viable option.
Abstract: Purpose: The purpose of this review is to address important methodological issues related to conducting accelerometer-based assessments of physical activity in free-living individuals. Methods: We review the extant scientific literature for empirical information related to the following issues: product selection, number of accelerometers needed, placement of accelerometers, epoch length, and days of monitoring required to estimate habitual physical activity. We also discuss the various options related to distributing and collecting monitors and strategies to enhance compliance with the monitoring protocol. Results: No definitive evidence exists currently to indicate that one make and model of accelerometer is more valid and reliable than another. Selection of accelerometer therefore remains primarily an issue of practicality, technical support, and comparability with other studies. Studies employing multiple accelerometers to estimate energy expenditure report only marginal improvements in explanatory power. Accelerometers are best placed on hip or the lower back. Although the issue of epoch length has not been studied in adults, the use of count cut points based on 1-min time intervals maybe inappropriate in children and may result in underestimation of physical activity. Among adults, 3-5 d of monitoring is required to reliably estimate habitual physical activity. Among children and adolescents, the number of monitoring days required ranges from 4 to 9 d, making it difficult to draw a definitive conclusion for this population. Face-to-face distribution and collection of accelerometers is probably the best option in field-based research, but delivery and return by express carrier or registered mail is a viable option. Conclusion: Accelerometer-based activity assessments requires careful planning and the use of appropriate strategies to increase compliance.
1,824 citations
12 Aug 2011
TL;DR: This paper presents a novel orientation algorithm designed to support a computationally efficient, wearable inertial human motion tracking system for rehabilitation applications, applicable to inertial measurement units (IMUs) consisting of tri-axis gyroscopes and accelerometers, and magnetic angular rate and gravity sensor arrays that also include tri- axis magnetometers.
Abstract: This paper presents a novel orientation algorithm designed to support a computationally efficient, wearable inertial human motion tracking system for rehabilitation applications. It is applicable to inertial measurement units (IMUs) consisting of tri-axis gyroscopes and accelerometers, and magnetic angular rate and gravity (MARG) sensor arrays that also include tri-axis magnetometers. The MARG implementation incorporates magnetic distortion compensation. The algorithm uses a quaternion representation, allowing accelerometer and magnetometer data to be used in an analytically derived and optimised gradient descent algorithm to compute the direction of the gyroscope measurement error as a quaternion derivative. Performance has been evaluated empirically using a commercially available orientation sensor and reference measurements of orientation obtained using an optical measurement system. Performance was also benchmarked against the propriety Kalman-based algorithm of orientation sensor. Results indicate the algorithm achieves levels of accuracy matching that of the Kalman based algorithm; < 0.8° static RMS error, < 1.7° dynamic RMS error. The implications of the low computational load and ability to operate at small sampling rates significantly reduces the hardware and power necessary for wearable inertial movement tracking, enabling the creation of lightweight, inexpensive systems capable of functioning for extended periods of time.
1,803 citations
Book•
23 Apr 1992TL;DR: This review discusses both new measures and new work on more well-established measures, both for use in specific diseases and for more general use, that are slowly being developed.
Abstract: Part 1 Background to the choice and use of measures: pathology, impairment, disability, handicap - a useful model measurement and assessment - what and why? classification of impairment, disability and handicap choosing a measure. Part 2 Measurement at different levels: measures of pathology motor and sensory impairments cognitive and emotional impairments personal physical disability global disability measures, extended ADL and social interaction handicap and quality of life. Part 3 Measurement in practice: measurement in some specific diseases measurement in some specific circumstances. Part 4 Measures for use in neurological disability: measures of cognitive impairment and disability measures of motor impairment measures of "focal" disability activities of daily living (ADL) and extended ADL tests global measures of disability measures of handicap and quality of life measures of emotion and social interaction multiple sclerosis stroke scales head injury Parkinson's disease and other movement disorders miscellaneous measures.
1,348 citations
TL;DR: Studies of phenomena such as cortical reorganization after a lesion, central nervous system repair, and the substantial enhancement of extremity use and linguistic function by behavioural therapy, support this emerging view.
Abstract: Recent discoveries about how the central nervous system responds to injury and how patients reacquire lost behaviours by training have yielded promising new therapies for neurorehabilitation. Until recently, this field had been largely static, but the current melding of basic behavioural science with neuroscience promises entirely new approaches to improving behavioural, perceptual and cognitive capabilities after neurological damage. Studies of phenomena such as cortical reorganization after a lesion, central nervous system repair, and the substantial enhancement of extremity use and linguistic function by behavioural therapy, support this emerging view. The ongoing changes in rehabilitation strategies might well amount to an impending paradigm shift in this field.
621 citations
TL;DR: A new easy-to-use software toolkit, DeepPoseKit, is introduced that addresses animal pose estimation problems using an efficient multi-scale deep-learning model, called Stacked DenseNet, and a fast GPU-based peak-detection algorithm for estimating keypoint locations with subpixel precision.
Abstract: Studying animal behavior can reveal how animals make decisions based on what they sense in their environment, but measuring behavior can be difficult and time-consuming. Computer programs that measure and analyze animal movement have made these studies faster and easier to complete. These tools have also made more advanced behavioral experiments possible, which have yielded new insights about how the brain organizes behavior. Recently, scientists have started using new machine learning tools called deep neural networks to measure animal behavior. These tools learn to measure animal posture – the positions of an animal’s body parts in space – directly from real data, such as images or videos, without being explicitly programmed with instructions to perform the task. This allows deep learning algorithms to automatically track the locations of specific animal body parts in videos faster and more accurately than previous techniques. This ability to learn from images also removes the need to attach physical markers to animals, which may alter their natural behavior. Now, Graving et al. have created a new deep learning toolkit for measuring animal behavior that combines components from previous tools with the latest advances in computer science. Simple modifications to how the algorithms are trained can greatly improve their performance. For example, adding connections between layers, or ‘neurons’, in the deep neural network and training the algorithm to learn the full geometry of the body – by drawing lines between body parts – both enhance its accuracy. As a result of adding these changes, the new toolkit can measure an animal's pose from previously unseen images with high speed and accuracy, after being trained on just 100 examples. Graving et al. tested their model on videos of fruit flies, zebras and locusts, and found that, after training, it was able to accurately track the animals’ movements. The new toolkit has an easy-to-use software interface and is freely available for other scientists to use and build on. The new toolkit may help scientists in many fields including neuroscience and psychology, as well as other computer scientists. For example, companies like Google and Apple use similar algorithms to recognize gestures, so making those algorithms faster and more efficient may make them more suitable for mobile devices like smartphones or virtual-reality headsets. Other possible applications include diagnosing and tracking injuries, or movement-related diseases in humans and livestock.
343 citations