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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...

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)

Complexity of human physiology and physical behavior has been suggested to decrease with aging and disease and make older adults more susceptible to falls. The present study investigates complexity in daily life walking in community-dwelling older adult fallers and non-fallers measured by a 3D inertial accelerometer sensor fixed to the lower back. Complexity was expressed using new metrics of entropy: refined composite multiscale entropy (RCME) and refined multiscale permutation entropy (RMPE). The study re-analyses data of 3 days daily-life activity originally described by Weiss et al. (2013) . The data set contains inertial sensor data from 39 older persons reporting less than 2 falls and 32 older persons reporting two or more falls during the previous year. The RCME and the RMPE were derived for trunk acceleration and velocity signals from walking epochs of 50 s using mean and variance coarse graining of the signals. Discriminant abilities of the entropy metrics were assessed using a partial least square discriminant analysis. Both RCME and RMPE successfully distinguished between the daily-life walking of the fallers and non-fallers (AUC>0.8) and performed better than the 35 conventional gait features investigated by Weiss et al. (2013) . Higher complexity was found in the vertical and mediolateral directions in the non-fallers for both entropy metrics. These findings suggest that RCME and RMPE can be used to improve the assessment of fall risk in older people.

The purpose of this study was to explore the role of visual and proprioceptive feedback in upper limb posture control in fibromyalgia (FM) and to assess the coherence between acceleration measurements of upper limb micro movements and surface electromyography (sEMG) of shoulder muscle activity (upper trapezius and deltoid). Twenty-five female FM patients and 25 age- and sex-matched healthy controls (HCs) performed three precision motor tasks: (1) maintain a steady shoulder abduction angle of 45° while receiving visual feedback about upper arm position and supporting external loads (0.5, 1, or 2kg), (2) maintain the same shoulder abduction angle without visual feedback (eyes closed) and no external loading, and (3) a joint position sense test (i.e., assessment of proprioceptive accuracy). Patients had more extensive increase in movement variance than HCs when visual feedback was removed (P

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.

Objectives The development of a reliable method for the identification of sedentary, light and moderate physical activities in older adults. The method consists of a validated set of definitions for the identification of the initiation and termination of physical activities performed by older adult participants, video recorded during free-living and a laboratory setting. Design Inter-rater reliability assessment in a fully crossed design. Methods An iterative consensus process was used to define the initiation and termination of common activities of daily living. These definitions were then tested using videos recorded in two scenarios (1) by 9 raters who annotated a video recording, of a free-living protocol in a home environment, recorded in a first person view, using a body-worn camera and (2) by 7 raters who annotated a video recording, of older adults performing a semi-structured protocol in a living-lab environment, recorded in a third person view, using wall mounted cameras. Results Inter-rater reliability was excellent for all items, with Krippendorff’s alpha and Fleiss’ kappa all above 0.84 and a percentage of agreement above 88%. All ICC(C,1) inter-rater values for the activity quantity and duration were all above 0.9. Conclusions This set of physical activity initiation and termination definitions offers independent researchers a gold standard method to allow for the consistent annotation of high-frequency video footage (25fps), in both a free-living and laboratory setting. When synchronised with body-worn or ambient sensors, this annotation will allow for the development and validation of physical activity classification systems to a higher resolution than before.

Detrended fluctuation analysis (DFA) is a popular method to numerically define the persistent structure of stride time variability. The conventional DFA assumes that the persistent structure in stride time variability is consistent in time and can be numerically defined by a single DFA scaling exponent. However, stride time regulation has to be adaptive to both environmental and internal perturbations and consequently, the persistent structure of stride time variability will have to be modulated in time. The present article introduces a new local detrended fluctuation analysis (DFAloc) that is able to detect modulation in the structure of stride time variability generated by phase-couplings between temporal scales. DFAloc was used in a reanalysis of the data set of stride time variability of Hausdorff et al. and a data set available at www.physionet.org. The results showed that there were significant phase couplings between temporal scales that generate an inverse correlation (r=-0.54 to -0.83) between the local structure and local magnitude of the stride time variability. Furthermore, the modulation of the local structure was significantly influenced by gait speed, external pace making, and age (all p's0.05). These results suggest several specific modifications of contemporary theories that have been suggested for the persistent structure of stride time variability found by the conventional DFA.

Empirical response time distributions from simple cognitive tasks are typically unimodal and positively skewed. In contrast, variance based scaling analyses, which have been used to study long-range dependency via the Hurst exponent, H > 0.5 , assume Gaussian response time distributions. This article presents a general method which can identify long-range trial dependency for response time series with power law distributions. The method fits an α -stable distribution to the response time series which satisfies a general version of the central limit theorem and consequently, an α -stable extension ( H q = 0 > 1 / α ) of long-range dependency. The method was used to reanalyze 96 response time series from three existing data sets which included simple reaction time, word naming, choice decision, and interval estimation tasks. The results showed that all response time distributions were appropriately modelled by an α -stable distribution. Furthermore, the response time series from the simple response and word naming tasks were not long-range dependent when the α -stable definition H q = 0 > 1 / α was used in place of the Gaussian response time distribution definition H q = 2 > 0.5 . The deviation between the two definitions of long-range dependency was shown to be caused by divergence of the variance for response time distributions with power-law decaying tails. The study concludes that the new α -stable definition, H q = 0 > 1 / α , of the long-range trial dependency should be used in the research of response time series instead of the Gaussian definition, H q = 2 > 0.5 .

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/).

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.

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 present article investigates gait stability of healthy older persons during weight transfer. Ten healthy older persons and ten younger persons walked 10 min each on a treadmill at 3 different gait speeds. The intra-stride change in gait stability was defined by the local divergence exponent λ(t) estimated by a newly developed method. The intra-stride changes in λ(t) during weight transfer were identified by separating each stride into a single and double support phase. The intra-stride changes in λ(t) were also compared to changes in the variation of the gait kinematics, i.e., SD(t). The healthy older persons walked at the same preferred walking speed as the younger persons. However, they exhibited significantly larger λ(t) (p0.001) during weight transfer in the double support phase. Local divergence was closely related to intra-stride changes in SD(t) of the feet in the anterior-posterior direction. Furthermore, a high correlation was found between local divergence and the variation in step length and step width for both older (R0.67, p0.05) and younger persons (R0.67, p0.05). The present results indicate that the gait kinematics of older adults are more dynamical unstable during the weight transfer compared to younger persons. Furthermore, a close relationship exists between intra-stride changes in dynamical stability and variation in step length and step width. Further work will validate the results of the present study using real-life perturbations of the gait kinematics of both younger and older adults.

It has been suggested that human behavior in general and cognitive performance in particular emerge from coordination between multiple temporal scales. In this article, we provide quantitative support for such a theory of interaction-dominant dynamics in human cognition by using wavelet-based multifractal analysis and accompanying multiplicative cascading process on the response series of 4 different cognitive tasks: simple response, word naming, choice decision, and interval estimation. Results indicated that the major portion of these response series had multiplicative interactions between temporal scales, visible as intermittent periods of large and irregular fluctuations (i.e., a multifractal structure). Comparing 2 component-dominant models of 1/f(alpha) fluctuations in cognitive performance with the multiplicative cascading process indicated that the multifractal structure could not be replicated by these component-dominant models. Furthermore, a similar multifractal structure was shown to be present in a model of self-organized criticality in the human nervous system, similar to a spatial extension of the multiplicative cascading process. These results illustrate that a wavelet-based multifractal analysis and the multiplicative cascading process form an appropriate framework to characterize interaction-dominant dynamics in human cognition. This new framework goes beyond the identification of 1/f(alpha) power laws and non-gaussian distributions in response series as used in previous studies. The present article provides quantitative support for a paradigm shift toward interaction-dominant dynamics in human cognition.

The present paper compares the performance of two Hilbert spectral analyses when applied to a synthetic RR series from a nonstationary integral pulse frequency modulation model and to real RR series from a dataset of normal sinus arrhythmia. The Hilbert-Huang transformation based on empirical mode decomposition is compared to the presently introduced Hilbert-Olhede-Walden transformation based on stationary wavelet packet decomposition. The comparison gives consistent results pointing to a superior performance of the Hilbert-Olhede-Walden transformation showing 33-163 times smaller deviations when estimating the instantaneous frequency traces of the synthetic RR series. Artificial fluctuations caused by mode mixing in the Hilbert-Huang spectrum are seen in both the synthetic and real RR series. It can be concluded that the instantaneous frequencies and amplitudes estimated by the Hilbert-Huang transformation should be interpreted with caution when investigating heart rate variability.

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%).

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.

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 sensitivity of 0.88 and a specificity of 0.87 in classifying falls correctly. This algorithm can be used distinguish real-world falls from normal activities of daily living in a sensor consisting of a tri-axial accelerometer and tri-axial gyroscope located at L5.

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.

In the present study we compared the performance of three different estimations of local dynamic stability λ to distinguish between the dynamics of the daily-life walking of elderly fallers and non-fallers. The study re-analyses inertial sensor data of 3-days daily-life activity originally described by Weiss et al. (2013). The data set contains inertial sensor data from 39 older persons who reported less than 2 falls and 31 older persons who reported two or more falls the previous year. 3D-acceleration and 3D-velocity signals from walking epochs of 50s were used to reconstruct a state space using three different methods. Local dynamic stability was estimated with the algorithms proposed by Rosenstein et al. (1993), Kantz (1994), and Ihlen et al. (2012a). Median λs assessed by Ihlen׳s and Kantz׳ algorithms discriminated better between elderly fallers and non-fallers (highest AUC=0.75 and 0.73) than Rosenstein׳s algorithm (highest AUC=0.59). The present results suggest that the ability of λ to distinguish between fallers and non-fallers is dependent on the parameter setting of the chosen algorithm. Further replication in larger samples of community-dwelling older persons and different patient groups is necessary before including the suggested parameter settings in fall risk assessment and prediction models.

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.

Automatic fall detection will reduce the consequences of falls in the elderly and promote independent living, ensuring people can confidently live safely at home. Inertial sensor technology can distinguish falls from normal activities. However

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/