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Physical activity has a strong influence on mental and physical health and is essential in healthy ageing and wellbeing for the ever-growing elderly population. Wearable sensors can provide a reliable and economical measure of activities of daily living (ADLs) by capturing movements through, e.g., accelerometers and gyroscopes. This study explores the potential of using classical machine learning and deep learning approaches to classify the most common ADLs: walking, sitting, standing, and lying. We validate the results on the ADAPT dataset, the most detailed dataset to date of inertial sensor data, synchronised with high frame-rate video labelled data recorded in a free-living environment from older adults living independently. The findings suggest that both approaches can accurately classify ADLs, showing high potential in profiling ADL patterns of the elderly population in free-living conditions. In particular, both long short-term memory (LSTM) networks and Support Vector Machines combined with ReliefF feature selection performed equally well, achieving around 97% F-score in profiling ADLs. This work was supported by the Mobilise-D project that has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No. 820820. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation program and the European Federation of Pharmaceutical Industries and Associations (EFPIA). Content in this publication reflects the authors’ view and neither IMI nor the European Union, EFPIA, or any Associated Partners are responsible for any use that may be made of the information contained herein.

ObjectivesTo determine whether videos taken by parents of their infants’ spontaneous movements were in accordance with required standards in the In-Motion-App, and whether the videos could be remotely scored by a trained General Movement Assessment (GMA) observer. Additionally, to assess the feasibility of using home-based video recordings for automated tracking of spontaneous movements, and to examine parents’ perceptions and experiences of taking videos in their homes.DesignThe study was a multi-centre prospective observational study.SettingParents/families of high-risk infants in tertiary care follow-up programmes in Norway, Denmark and Belgium.MethodsParents/families were asked to video record their baby in accordance with the In-Motion standards which were based on published GMA criteria and criteria covering lighting and stability of smartphone. Videos were evaluated as GMA ‘scorable’ or ‘non-scorable’ based on predefined criteria. The accuracy of a 7-point body tracker software was compared with manually annotated body key points. Parents were surveyed about the In-Motion-App information and clarity.ParticipantsThe sample comprised 86 parents/families of high-risk infants.ResultsThe 86 parent/families returned 130 videos, and 121 (96%) of them were in accordance with the requirements for GMA assessment. The 7-point body tracker software detected more than 80% of body key point positions correctly. Most families found the instructions for filming their baby easy to follow, and more than 90% reported that they did not become more worried about their child’s development through using the instructions.ConclusionsThis study reveals that a short instructional video enabled parents to video record their infant’s spontaneous movements in compliance with the standards required for remote GMA. Further, an accurate automated body point software detecting infant body landmarks in smartphone videos will facilitate clinical and research use soon. Home-based video recordings could be performed without worrying parents about their child’s development.Trials registration numberNCT03409978.

Background Impaired postural control is a key feature of cerebral palsy that affects daily living. Measures of trunk movement and acceleration have been used to assess dynamic postural control previously. In many children with cerebral palsy, ankle-foot orthoses are used to provide a stable base of support, but their effect on postural control is not yet understood. Objectives The objectives of the current study were to investigate the effects of ankle-foot orthoses on postural control and energy cost of walking in children with cerebral palsy. Study design Clinical study with controls. Methods Trunk accelerometry (amplitude and structure) and energy cost of walking (J/kg/m) were recorded from five-minute walking trials with and without ankle-foot orthoses for children with cerebral palsy and without orthoses for the reference group. Results Nineteen children with unilateral spastic cerebral palsy and fourteen typically developed children participated. The use of ankle-foot orthoses increased structure complexity of trunk acceleration in mediolateral and anterior-posterior directions. The use of ankle-foot orthoses changed mediolateral-structure toward values found in typically developed children. This change was not associated with a change in energy cost during walking. Conclusions The use of ankle-foot orthoses does affect trunk acceleration that may indicate a beneficial effect on postural control. Using measures of trunk acceleration may contribute to clinical understanding on how the use of orthoses affect postural control.

Assessment of spontaneous movements can predict the long-term developmental disorders in high-risk infants. In order to develop algorithms for automated prediction of later disorders, highly precise localization of segments and joints by infant pose estimation is required. Four types of convolutional neural networks were trained and evaluated on a novel infant pose dataset, covering the large variation in 1 424 videos from a clinical international community. The localization performance of the networks was evaluated as the deviation between the estimated keypoint positions and human expert annotations. The computational efficiency was also assessed to determine the feasibility of the neural networks in clinical practice. The best performing neural network had a similar localization error to the inter-rater spread of human expert annotations, while still operating efficiently. Overall, the results of our study show that pose estimation of infant spontaneous movements has a great potential to support research initiatives on early detection of developmental disorders in children with perinatal brain injuries by quantifying infant movements from video recordings with human-level performance., Published in Computerized Medical Imaging and Graphics (CMIG)

Extensive test batteries are often needed to obtain a comprehensive picture of a person&rsquo, s functional status. Many test batteries are not suitable for active and healthy adults due to ceiling effects, or require a lot of space, time, and training. The Community Balance and Mobility Scale (CBMS) is considered a gold standard for this population, but the test is complex, as well as time- and resource intensive. There is a strong need for a faster, yet sensitive and robust test of physical function in seniors. We sought to investigate whether an instrumented Timed Up and Go (iTUG) could predict the CBMS score in 60 outpatients and healthy community-dwelling seniors, where features of the iTUG were predictive, and how the prediction of CBMS with the iTUG compared to standard clinical tests. A partial least squares regression analysis was used to identify latent components explaining variation in CBMS total score. The model with iTUG features was able to predict the CBMS total score with an accuracy of 85.2% (84.9&ndash, 85.5%), while standard clinical tests predicted 82.5% (82.2&ndash, 82.8%) of the score. These findings suggest that a fast and easily administered iTUG could be used to predict CBMS score, providing a valuable tool for research and clinical care.

Single-person human pose estimation facilitates markerless movement analysis in sports, as well as in clinical applications. Still, state-of-the-art models for human pose estimation generally do not meet the requirements of real-life applications. The proliferation of deep learning techniques has resulted in the development of many advanced approaches. However, with the progresses in the field, more complex and inefficient models have also been introduced, which have caused tremendous increases in computational demands. To cope with these complexity and inefficiency challenges, we propose a novel convolutional neural network architecture, called EfficientPose, which exploits recently proposed EfficientNets in order to deliver efficient and scalable single-person pose estimation. EfficientPose is a family of models harnessing an effective multi-scale feature extractor and computationally efficient detection blocks using mobile inverted bottleneck convolutions, while at the same time ensuring that the precision of the pose configurations is still improved. Due to its low complexity and efficiency, EfficientPose enables real-world applications on edge devices by limiting the memory footprint and computational cost. The results from our experiments, using the challenging MPII single-person benchmark, show that the proposed EfficientPose models substantially outperform the widely-used OpenPose model both in terms of accuracy and computational efficiency. In particular, our top-performing model achieves state-of-the-art accuracy on single-person MPII, with low-complexity ConvNets., Comment: Published in Applied Intelligence Journal (APIN)

Background: Early identification of cerebral palsy (CP) during infancy will provide opportunities for early therapies and treatments. The aim of the present study was to present a novel machine-learning model, the Computer-based Infant Movement Assessment (CIMA) model, for clinically feasible early CP prediction based on infant video recordings. Methods: The CIMA model was designed to assess the proportion (%) of CP risk-related movements using a time&ndash, frequency decomposition of the movement trajectories of the infant&rsquo, s body parts. The CIMA model was developed and tested on video recordings from a cohort of 377 high-risk infants at 9&ndash, 15 weeks corrected age to predict CP status and motor function (ambulatory vs. non-ambulatory) at mean 3.7 years age. The performance of the model was compared with results of the general movement assessment (GMA) and neonatal imaging. Results: The CIMA model had sensitivity (92.7%) and specificity (81.6%), which was comparable to observational GMA or neonatal cerebral imaging for the prediction of CP. Infants later found to have non-ambulatory CP had significantly more CP risk-related movements (median: 92.8%, p = 0.02) compared with those with ambulatory CP (median: 72.7%). Conclusion: The CIMA model may be a clinically feasible alternative to observational GMA.

Background: Early prediction of cerebral palsy (CP) using the General Movement Assessment (GMA) during the fidgety movements (FM) period has been recommended as standard of care in high-risk infants. The aim of this study was to determine the accuracy of GMA, alone or in combination with neonatal imaging, in predicting cerebral palsy (CP). Methods: Infants with increased risk of perinatal brain injury were prospectively enrolled from 2009&ndash, 2014 in this multi-center, observational study. FM were classified by two certified GMA observers blinded to the clinical history. Abnormal GMA was defined as absent or sporadic FM. CP-status was determined by clinicians unaware of GMA results. Results: Of 450 infants enrolled, 405 had scorable video and follow-up data until at least 18&ndash, 24 months. CP was confirmed in 42 (10.4%) children at mean age 3 years 1 month. Sensitivity, specificity, positive and negative predictive values, and accuracy of absent/sporadic FM for CP were 76.2, 82.4, 33.3, 96.8, and 81.7%, respectively. Only three (8.1%) of 37 infants with sporadic FM developed CP. The highest accuracy (95.3%) was achieved by a combination of absent FM and abnormal neonatal imaging. Conclusion: In infants with a broad range of neonatal risk factors, accuracy of early CP prediction was lower for GMA than previously reported but increased when combined with neonatal imaging. Sporadic FM did not predict CP in this study.

Physical activity is strongly linked with mental and physical health in the elderly population and accurate monitoring of activities of daily living (ADLs) can help improve quality of life and well-being. This study presents and validates an inertial sensors-based physical activity classification system developed with older adults as the target population. The dataset was collected in free living conditions without placing constraints on the way and order of performing ADLs. Four sensor locations (chest, lower back, wrist, and thigh) were explored to obtain the optimal number and combination of sensors by finding the best tradeoff between the system's performance and wearability. Several feature selection techniques were implemented on the feature set obtained from acceleration and angular velocity signals to classify four major ADLs (sitting, standing, walking, and lying). Support vector machine was used for the classification of the ADLs. The findings show the potential of different solutions (single-sensor or multi-sensor) to correctly classify the ADLs of older people in free living conditions. Considering a minimal set-up of a single sensor, the sensor worn at the L5 achieved the best performance. A two-sensor solution (L5 + thigh) achieved a better performance with respect to a single-sensor solution. On the other hand, considering more than two sensors did not provide further improvements. Finally, we evaluated the computational cost of different solutions and it was shown that a feature selection step can reduce the computational cost of the system and increase the system performance in most cases. This can be helpful for real-time applications.

Background: Previous evidence suggests that the variability of the spatial center of infant movements, calculated by computer-based video analysis software, can identify fidgety general movements (GMs) and predict cerebral palsy. Aim: To evaluate whether computer-based video analysis quantifies specific characteristics of normal fidgety movements as opposed to writhing general movements. Methods: A longitudinal study design was applied. Twenty-seven low-to moderate-risk preterm infants (20 boys, 7 girls; mean gestational age 32 [SD 2.7, range 27–36] weeks, mean birth weight 1790 grams [SD 430g, range 1185–2700g]) were videotaped at the ages of 3–5 weeks (period of writhing GMs) and 10–15 weeks (period of fidgety GMs) post term. GMs were classified according to Prechtl’s general movement assessment method (GMA) and by computer-based video analysis. The variability of the centroid of motion (CSD), derived from differences between subsequent video frames, was calculated by means of computer-based video analysis software; group mean differences between GM periods were reported. Results: The mean variability of the centroid of motion (CSD) determined by computer-based video analysis was 7.5% lower during the period of fidgety GMs than during the period of writhing GMs (p = 0.004). Conclusion: Our findings support that the variability of the centroid of motion reflects small and variable movements evenly distributed across the body, and hence shows that computer-based video analysis qualifies for assessment of direction and amplitude of FMs in young infants. This is an [Accepted/Original Manuscript] of an article published by Taylor & Francis in [Physiotherapy Theory and Practice] on [24 Oct 2017], available at http://www.tandfonline.com/doi/full/10.1080/09593985.2017.1391908. Locked until 24.10.2018 due to copyright restrictions.

Physical activity monitoring algorithms are often developed using conditions that do not represent real-life activities, not developed using the target population, or not labelled to a high enough resolution to capture the true detail of human movement. We have designed a semi-structured supervised laboratory-based activity protocol and an unsupervised free-living activity protocol and recorded 20 older adults performing both protocols while wearing up to 12 body-worn sensors. Subjects’ movements were recorded using synchronised cameras (≥25 fps), both deployed in a laboratory environment to capture the in-lab portion of the protocol and a body-worn camera for out-of-lab activities. Video labelling of the subjects’ movements was performed by five raters using 11 different category labels. The overall level of agreement was high (percentage of agreement >90.05%, and Cohen’s Kappa, corrected kappa, Krippendorff’s alpha and Fleiss’ kappa >0.86). A total of 43.92 h of activities were recorded, including 9.52 h of in-lab and 34.41 h of out-of-lab activities. A total of 88.37% and 152.01% of planned transitions were recorded during the in-lab and out-of-lab scenarios, respectively. This study has produced the most detailed dataset to date of inertial sensor data, synchronised with high frame-rate (≥25 fps) video labelled data recorded in a free-living environment from older adults living independently. This dataset is suitable for validation of existing activity classification systems and development of new activity classification algorithms. © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

Despite frequent use of exergames in intervention studies to improve physical function in older adults, we lack knowledge about the movements performed during exergaming. This causes difficulties for interpreting results of intervention studies and drawing conclusions about the efficacy of exergames to exercise specific functions important for the elderly population. The aim of the current study was to investigate whether game and game level affect older adults’ stepping and upper body movements while playing stepping exergames. A 3D-motion capture experiment was performed with 20 elderly (12 women and 8 men; age range 65–90 years), playing two exergames, The Mole from SilverFit and LightRace in YourShape: Fitness Evolved, on two difficulty levels, with five 1-min trials for each game and level. Reflective markers were placed on bases of first toe, heels, and lower back. Movement characteristics were analyzed with a linear mixed model. Results indicated that both game and game level affected movement characteristics. Participants took shorter steps and had lower step velocity when playing The Mole compared to LightRace, while The Mole prompted more variation in step length and step velocity. Compared to LightRace, The Mole elicited larger upper body movements in both ML- and AP-directions and participants’ feet and upper body covered a larger area. Increasing difficulty level from Easy to Medium resulted in overall decrease of movement, except for number of steps and step speed when playing LightRace. Even with only two games, two levels, and five trials at each, this study indicates that the choice of exergame is not indifferent when aiming to exercise specific functions in older adults and that exergames need to be chosen and designed carefully based on the goals of the intervention. © 2016 Skjæret-Maroni, Vonstad, Ihlen, Tan, Helbostad and Vereijken. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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 older subjects (76.4 ± 5.6 years). The performance in classifying four basic activities of daily life (sitting, standing, walking, and lying) is analyzed in controlled and free living conditions. To observe the performance of laboratory-based systems in field-based conditions, we trained the activity classification systems using data recorded in a laboratory environment and tested them in real-life conditions in the field. The findings show that the performance of all systems trained with data in the laboratory setting highly deteriorates when tested in real-life conditions, thus highlighting the need to train and test the classification systems in the real-life setting. Moreover, we tested the sensitivity of chosen systems to window size (from 1 s to 10 s) suggesting that overall accuracy decreases with increasing window size. Finally, to evaluate the impact of the number of sensors on the performance, chosen systems are modified considering only the sensing unit worn at the lower back. The results, similarly to the multi-sensor setup, indicate substantial degradation of the performance when laboratory-trained systems are tested in the real-life setting. This degradation is higher than in the multi-sensor setup. Still, the performance provided by the single-sensor approach, when trained and tested with real data, can be acceptable (with an accuracy above 80%).

Background:Restoration of gait is an important goal of rehabilitation after hip fracture. Numerous spatial andtemporal gait variables have been reported in the literature, but beyond gait speed, there is little agreement onwhich gait variables should be reported and which are redundant in describing gait recovery following hip fracture.The aims of this study were to identify distinct domains of gait and key variables representing these domains, andto explore how known predictors of poor outcome after hip fracture were associated with these key variables.Methods:Spatial and temporal gait variables were collected four months following hip fracture in 249 participantsusing an electronic walkway (GAITRite®). From the initial set of 31 gait variables, 16 were selected following asystematic procedure. An explorative factor analysis with oblique (oblimin) rotation was performed, using principalcomponent analysis for extraction of factors. Unique domains of gait and the variable best representing thesedomains were identified. Multiple regression analyses including six predictors; age, gender, fracture type, pain,global cognitive function and grip strength were performed for each of the identified key gait variables.Results:Mean age of participants was 82.6 (SD = 6.0) years, 75 % were women, and mean gait speed was 0.6(SD = 0.2) m/sec. The factor analysis revealed four distinct gait domains, and the key variables that best representedthese domains were double support time, walk ratio, variability of step velocity, and single support asymmetry.Cognitive decline, low grip strength, extra capsular fracture and male gender, but not pain or age, were significantpredictors of impaired gait.Conclusions:This work proposes four key variables to represent gait of older people after hip fracture. These corevariables were associated with known predictors of poor outcome after hip fracture and should warrant furtherassessment to confirm their importance as outcome variables in addition to gait speed. © 2015 Thingstad et al.Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link tothe Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

The present study compares phase-dependent measures of local dynamic stability of daily life walking with 35 conventional gait features in their ability to discriminate between community-dwelling older fallers and nonfallers. The study reanalyzes 3D-acceleration data of 3-day daily life activity from 39 older people who reported less than 2 falls during one year and 31 who reported two or more falls. Phase-dependent local dynamic stability was defined for initial perturbation at 0%, 20%, 40%, 60%, and 80% of the step cycle. A partial least square discriminant analysis (PLS-DA) was used to compare the discriminant abilities of phase-dependent local dynamic stability with the discriminant abilities of 35 conventional gait features. The phase-dependent local dynamic stability λ at 0% and 60% of the step cycle discriminated well between fallers and nonfallers (AUC = 0.83) and was significantly larger () for the nonfallers. Furthermore, phase-dependent λ discriminated as well between fallers and nonfallers as all other gait features combined. The present result suggests that phase-dependent measures of local dynamic stability of daily life walking might be of importance for further development in early fall risk screening tools. Copyright © 2015 Espen A. F. Ihlen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Analyses of response time series haveprovided insight into mental organiza-tionandcognitiveprocessesusedinawide variety of tasks such as simple reac-tion time, word naming, choice decision,visual search, memory search, and lexi-cal decision (Gilden, 2001). One of thenew and frequently used sets of analy-ses is the numerical definition of scaleinvariantstructureofresponsetimeseries,also called 1/

Existing methods to assess inter-joint coordination in human walking have several important weaknesses. These methods are unable to define 1) the instantaneous changes in coordination within the stride cycle, 2) coordination between multiple joints, or 3) the coupling strength of joint rotations rather than their phase relationships. The present paper introduces a new method called generalized wavelet coherence analysis (GWCA) that solves these three fundamental limitations of previous methods. GWCA combines wavelet coherence analysis with a matrix correlation method to define instantaneous correlation coefficients as the coupling strength for an arbitrary number of joint rotations. The main purpose of the present study is to develop GWCA to quantify inter-joint coordination and thereby assess age-related differences in the coordination of human gaits. Nine young and 19 healthy older persons walked 5 min on a treadmill at three different gait speeds. Joint rotations of the lower extremities were assessed by a Vicon three-dimensional motion capture system. The main results indicated that the older group had significant weaker correlations ( t-tests: P < 0.0001) in the preswing phase compared with the younger group for all gait speeds. The age-related differences in inter-joint coordination were more pronounced than the age-related differences in rotations of the individual joints. The intra-stride changes in inter-joint coordination were in agreement with recent findings of intra-stride modulations in neural activity in the sensorimotor cortex. Thus change in the inter-joint coordination assessed by GWCA might be an early indicator of functional decline.

Response time series with a non-Gaussian distribution and long-range dependent dynamics have been reported for several cognitive tasks. Conventional monofractal analyses numerically define a long-range dependency as a single scaling exponent, but they assume that the response times are Gaussian distributed. Ihlen and Vereijken (Journal of Experimental Psychology: General, 139, 436-463, 2010) suggested multifractal extensions of the conventional monofractal analyses that are more suitable when the response time has a non-Gaussian distribution. Multifractal analyses estimate a multifractal spectrum of scaling exponents that contain the single exponent estimated by the conventional monofractal analyses. However, a comparison of the performance of multifractal analyses with behavioral variables has not yet been addressed. The present study compares the performance of seven multifractal analyses. The multifractal analyses were tested on multiplicative cascading noise that generates time series with a predefined multifractal spectrum and with a structure of variation that mimics intermittent response time variation. Time series with 1,024 and 4,096 samples were generated with additive noise and multiharmonic trends of two different magnitudes (signal-to-noise/trend ratio; 0.33 and 1). The results indicate that all multifractal analysis has individual pros and cons related to sample size, multifractality, and the presence of additive noise and trends in the response time series. The summary of pros and cons of the seven multifractal analyses provides a guideline for the choice of multifractal analyses of response time series and other behavioral variables.

Physiological and behavioural phenomena are often complex, characterized by variations in time series. Variations in time series reflect how these phenomena organize into coherent structures by interactions that span multiple scales in both time and space. The present tutorial is an introduction to multifractal analyses that can identify these scale invariant interactions within time series by its multifractal spectrum. The multifractal spectrum can be estimated directly from scale-dependent measurements or from its q-order statistics. The tutorial emphasizes the most common scale-dependent measurements defined by the wavelet transforms and the detrended fluctuation analyses. The tutorial also emphasizes common features of all multifractal analyses, like the choice of linear regression method, scaling range and elimination of spurious singularities, which are important for a robust estimation of the multifractal spectrum. The tutorial ends with two brief examples where multifractal analyses are employed to time series from multifractal models and the complex phenomena of cognitive performance. References to available software for multifractal analyses are included at the end of the tutorial. The main aim of the tutorial is to give the reader an introduction to multifractal analyses without the extensive technicalities typically provided in mathematical journals.