Direct measurement of human movement by accelerometry

Assessment of Homomorphic Analysis for Human Activity Recognition From Acceleration Signals

Unobtrusive activity monitoring can provide valuable information for medical and sports applications. In recent years, human activity recognition has moved to wearable sensors to deal with unconstrained scenarios. Accelerometers are the preferred sensors due to their simplicity and availability. Previous studies have examined several classic techniques for extracting features from acceleration signals, including time-domain, time-frequency, frequency-domain, and other heuristic features. Spectral and temporal features are the preferred ones and they are generally computed from acceleration components, leaving the acceleration magnitude potential unexplored. In this study, a new type of feature extraction stage, based on homomorphic analysis, is proposed in order to exploit discriminative activity information present in acceleration signals. Homomorphic analysis can isolate the information about whole body dynamics and translate it into a compact representation, called cepstral coefficients. Experiments have explored several configurations of the proposed features, including size of representation, signals to be used, and fusion with other features. Cepstral features computed from acceleration magnitude obtained one of the highest recognition rates. In addition, a beneficial contribution was found when time-domain and moving pace information was included in the feature vector. Overall, the proposed system achieved a recognition rate of 91.21% on the publicly available SCUT-NAA dataset. To the best of our knowledge, this is the highest recognition rate on this dataset.

Wearable Sensor Data Classification for Human Activity Recognition Based on an Iterative Learning Framework

The design of multiple human activity recognition applications in areas such as healthcare, sports and safety relies on wearable sensor technologies. However, when making decisions based on the data acquired by such sensors in practical situations, several factors related to sensor data alignment, data losses, and noise, among other experimental constraints, deteriorate data quality and model accuracy. To tackle these issues, this paper presents a data-driven iterative learning framework to classify human locomotion activities such as walk, stand, lie, and sit, extracted from the Opportunity dataset. Data acquired by twelve 3-axial acceleration sensors and seven inertial measurement units are initially de-noised using a two-stage consecutive filtering approach combining a band-pass Finite Impulse Response (FIR) and a wavelet filter. A series of statistical parameters are extracted from the kinematical features, including the principal components and singular value decomposition of roll, pitch, yaw and the norm of the axial components. The novel interactive learning procedure is then applied in order to minimize the number of samples required to classify human locomotion activities. Only those samples that are most distant from the centroids of data clusters, according to a measure presented in the paper, are selected as candidates for the training dataset. The newly built dataset is then used to train an SVM multi-class classifier. The latter will produce the lowest prediction error. The proposed learning framework ensures a high level of robustness to variations in the quality of input data, while only using a much lower number of training samples and therefore a much shorter training time, which is an important consideration given the large size of the dataset.

Quantitative Analysis of Motor Status in Parkinson's Disease Using Wearable Devices: From Methodological Considerations to Problems in Clinical Applications

Long-term and objective monitoring is necessary for full assessment of the condition of patients with Parkinson's disease (PD). Recent advances in biotechnology have seen the development of various types of wearable (body-worn) sensor systems. By using accelerometers and gyroscopes, these devices can quantify motor abnormalities, including decreased activity and gait disturbances, as well as nonmotor signs, such as sleep disturbances and autonomic dysfunctions in PD. This review discusses methodological problems inherent in wearable devices. Until now, analysis of the mean values of motion-induced signals on a particular day has been widely applied in the clinical management of PD patients. On the other hand, the reliability of these devices to detect various events, such as freezing of gait and dyskinesia, has been less than satisfactory. Quantification of disease-specific changes rather than nonspecific changes is necessary.

A Physical Activity Reference Data-Set Recorded from Older Adults Using Body-Worn Inertial Sensors and Video Technology-The ADAPT Study Data-Set

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.

A triboelectric motion sensor in wearable body sensor network for human activity recognition

The goal of this study is to design a novel triboelectric motion sensor in wearable body sensor network for human activity recognition. Physical activity recognition is widely used in well-being management, medical diagnosis and rehabilitation. Other than traditional accelerometers, we design a novel wearable sensor system based on triboelectrification. The triboelectric motion sensor can be easily attached to human body and collect motion signals caused by physical activities. The experiments are conducted to collect five common activity data: sitting and standing, walking, climbing upstairs, downstairs, and running. The k-Nearest Neighbor (kNN) clustering algorithm is adopted to recognize these activities and validate the feasibility of this new approach. The results show that our system can perform physical activity recognition with a successful rate over 80% for walking, sitting and standing. The triboelectric structure can also be used as an energy harvester for motion harvesting due to its high output voltage in random low-frequency motion.

Neuronal coding of auditory sensorimotor gating in medial prefrontal cortex

The medial prefrontal cortex (mPFC) is thought to be an essential brain region for sensorimotor gating. The exact neuronal mechanisms, however, have not been extensively investigated yet by delicate single unit recording methods Prepulse inhibition (PPI) of the startle response is a broadly used important tool to investigate the inhibitory processes of sensorimotor gating. The present study was designed to examine the neuronal mechanisms of sensorimotor gating in the mPFC in freely moving rats. In these experiments, the animals were subjected to both pulse alone and prepulse+pulse stimulations. Head acceleration and the neuronal activity of the mPFC were simultaneously recorded. To adequately measure the startle reflex, a new headstage with 3D-accelerometer was created. The duration of head acceleration was longer in pulse alone trials than in prepulse+pulse trial conditions, and the amplitude of head movements was significantly larger during the pulse alone than during the prepulse+pulse situations. Single unit activities in the mPFC were recorded by means of chronically implanted tetrodes during acoustic stimulation evoked startle response and PPI. High proportion of medial prefrontal cortical neurons responded to these stimulations by characteristic firing patterns: short duration equal and unequal excitatory, medium duration excitatory, and long duration excitatory and inhibitory responses were recorded. The present findings, first time in the literature, demonstrated the startle and PPI elicited neuronal activity changes of the mPFC, and thus, provided evidence for a key role of this limbic forebrain area in sensorimotor gating process.

Clinical validation of a body-fixed 3D accelerometer and algorithm for activity monitoring in orthopaedic patients

Background/Objective

Activity is increasingly being recognized as a highly relevant parameter in all areas of healthcare for diagnosis, treatment, or outcome assessment, especially in orthopaedics where the movement apparatus is directly affected. Therefore, the aim of this study was to develop, describe, and clinically validate a generic activity-monitoring algorithm, satisfying a combination of three criteria. The algorithm must be able to identify, count, and time a large set of relevant daily activities. It must be validated for orthopaedic patients as well as healthy individuals, and the validation must be in a setting that mimics free-living conditions.

Methods

Using various technical solutions, such as a dual-axis approach, dynamic inclinometry (hip flexion), and semiautomatic calibration (gait speed), the algorithms were designed to count and time the following postures, transfers, and activities of daily living: resting/sitting, standing, walking, ascending and descending stairs, sit-stand transitions, and cycling. In addition, the number of steps per walking bout was determined. Validation was performed with healthy individuals and patients who had undergone unilateral total joint arthroplasty, representing a wide spectrum of functional capacity. Video observation was used as the gold standard to count and time activities in a validation protocol approaching free-living conditions.

Results

In total 992 and 390 events (activities or postures) were recorded in the healthy group and patient group, respectively. The mean error varied between 0% and 2.8% for the healthy group and between 0% and 7.5% for the patient group. The error expressed in percentage of time varied between 2.0% and 3.0% for both groups.

Conclusion

Activity monitoring of orthopaedic patients by counting and timing a large set of relevant daily life events is feasible in a user- and patient-friendly way and at high clinical validity using a generic three-dimensional accelerometer and algorithms based on empirical and physical methods. The algorithms performed well for healthy individuals as well as patients recovering after total joint replacement in a challenging validation set-up. With such a simple and transparent method real-life activity parameters can be collected in orthopaedic practice for diagnostics, treatments, outcome assessment, or biofeedback.

Application of data fusion techniques and technologies for wearable health monitoring

Technological advances in sensors and communications have enabled discrete integration into everyday objects, both in the home and about the person. Information gathered by monitoring physiological, behavioural, and social aspects of our lives, can be used to achieve a positive impact on quality of life, health, and well-being. Wearable sensors are at the cusp of becoming truly pervasive, and could be woven into the clothes and accessories that we wear such that they become ubiquitous and transparent. To interpret the complex multidimensional information provided by these sensors, data fusion techniques are employed to provide a meaningful representation of the sensor outputs. This paper is intended to provide a short overview of data fusion techniques and algorithms that can be used to interpret wearable sensor data in the context of health monitoring applications. The application of these techniques are then described in the context of healthcare including activity and ambulatory monitoring, gait analysis, fall detection, and biometric monitoring. A snap-shot of current commercially available sensors is also provided, focusing on their sensing capability, and a commentary on the gaps that need to be bridged to bring research to market.

Trunk Motion System (TMS) Using Printed Body Worn Sensor (BWS) via Data Fusion Approach

Human movement analysis is an important part of biomechanics and rehabilitation, for which many measurement systems are introduced. Among these, wearable devices have substantial biomedical applications, primarily since they can be implemented both in indoor and outdoor applications. In this study, a Trunk Motion System (TMS) using printed Body-Worn Sensors (BWS) is designed and developed. TMS can measure three-dimensional (3D) trunk motions, is lightweight, and is a portable and non-invasive system. After the recognition of sensor locations, twelve BWSs were printed on stretchable clothing with the purpose of measuring the 3D trunk movements. To integrate BWSs data, a neural network data fusion algorithm was used. The outcome of this algorithm along with the actual 3D anatomical movements (obtained by Qualisys system) were used to calibrate the TMS. Three healthy participants with different physical characteristics participated in the calibration tests. Seven different tasks (each repeated three times) were performed, involving five planar, and two multiplanar movements. Results showed that the accuracy of TMS system was less than 1.0°, 0.8°, 0.6°, 0.8°, 0.9°, and 1.3° for flexion/extension, left/right lateral bending, left/right axial rotation, and multi-planar motions, respectively. In addition, the accuracy of TMS for the identified movement was less than 2.7°. TMS, developed to monitor and measure the trunk orientations, can have diverse applications in clinical, biomechanical, and ergonomic studies to prevent musculoskeletal injuries, and to determine the impact of interventions.

Evaluation of the performance of accelerometer-based gait event detection algorithms in different real-world scenarios using the MAREA gait database

Numerous gait event detection (GED) algorithms have been developed using accelerometers as they allow the possibility of long-term gait analysis in everyday life. However, almost all such existing algorithms have been developed and assessed using data collected in controlled indoor experiments with pre-defined paths and walking speeds. On the contrary, human gait is quite dynamic in the real-world, often involving varying gait speeds, changing surfaces and varying surface inclinations. Though portable wearable systems can be used to conduct experiments directly in the real-world, there is a lack of publicly available gait datasets or studies evaluating the performance of existing GED algorithms in various real-world settings. This paper presents a new gait database called MAREA (n=20 healthy subjects) that consists of walking and running in indoor and outdoor environments with accelerometers positioned on waist, wrist and both ankles. The study also evaluates the performance of six state-of-the-art accelerometer-based GED algorithms in different real-world scenarios, using the MAREA gait database. The results reveal that the performance of these algorithms is inconsistent and varies with changing environments and gait speeds. All algorithms demonstrated good performance for the scenario of steady walking in a controlled indoor environment with a combined median F1score of 0.98 for Heel-Strikes and 0.94 for Toe-Offs. However, they exhibited significantly decreased performance when evaluated in other lesser controlled scenarios such as walking and running in an outdoor street, with a combined median F1score of 0.82 for Heel-Strikes and 0.53 for Toe-Offs. Moreover, all GED algorithms displayed better performance for detecting Heel-Strikes as compared to Toe-Offs, when evaluated in different scenarios.

A study of wrist-worn activity measurement as a potential real-world biomarker for late-life depression

BACKGROUND

Late-life depression (LLD) is associated with a decline in physical activity. Typically this is assessed by self-report questionnaires and, more recently, with actigraphy. We sought to explore the utility of a bespoke activity monitor to characterize activity profiles in LLD more precisely.

METHOD

The activity monitor was worn for 7 days by 29 adults with LLD and 30 healthy controls. Subjects underwent neuropsychological assessment and quality of life (QoL) (36-item Short-Form Health Survey) and activities of daily living (ADL) scales (Instrumental Activities of Daily Living Scale) were administered.

RESULTS

Physical activity was significantly reduced in LLD compared with controls (t = 3.63, p < 0.001), primarily in the morning. LLD subjects showed slower fine motor movements (t = 3.49, p < 0.001). In LLD patients, activity reductions were related to reduced ADL (r = 0.61, p < 0.001), lower QoL (r = 0.65, p < 0.001), associative learning (r = 0.40, p = 0.036), and higher Montgomery-Åsberg Depression Rating Scale score (r = -0.37, p < 0.05).

CONCLUSIONS

Patients with LLD had a significant reduction in general physical activity compared with healthy controls. Assessment of specific activity parameters further revealed the correlates of impairments associated with LLD. Our study suggests that novel wearable technology has the potential to provide an objective way of monitoring real-world function.

Respiratory muscle strength in patients with pulmonary hypertension: The relationship with exercise capacity, physical activity level, and quality of life

INTRODUCTION

Pulmonary hypertension (PH) is characterized by exertional dyspnea, fatigue, chest pain, dizziness, and syncope. Physical activity, peripheral, and respiratory muscle strength reduces in pateints with PH. Little is known about respiratory muscle weakness and related outcomes.

OBJECTIVES

The aims of the study were to determine respiratory muscle strength and to investigate the relationship between respiratory muscle strength and spirometric measurements, exercise capacity, physical activity level, quality of life, and pulmonary hemodynamics in patients with PH.

METHODS

In total, 33 patients aged 25-80 years who were diagnosed as having PH and 24 healthy volunteers were included in the study. To measure respiratory function, spirometry, maximal inspiratory (MIP), and expiratory pressures (MEP) were used. Physical activity level was determined with activity monitoring (SenseWear Armband) and the International Physical Activity Questionnaire-Short Form. Exercise capacity was determined using the 6-minute walk test. Quality of life was evaluated with the Minnesota Living with Heart Failure Questionnaire (MLHFQ).

RESULTS

Maximal inspiratory pressure and MEP values of the patients with PH were significantly lower than the age- and sex-matched healthy controls (P < .0001). Significant relationships were found between the MIP and six MWD (r = .40, P = .02), vigorous physical activity (r = .38, P = .03), moderate physical activity (r = 61, P < .001), and arm band-average metabolic equivalent (r = .39, P = .02).

CONCLUSION

The relationship between maximum inspiratory pressure, exercise capacity, and physical activity level showed that a decrease in exercise capacity or physical activity level may be a predictor for decreased MIP.

Gait-force model and inertial measurement unit-based measurements: A new approach for gait analysis and balance monitoring

Background/Objective

This work describes a new approach for gait analysis and balance measurement. It uses an inertial measurement unit (IMU) that can either be embedded inside a dynamically unstable platform for balance measurement or mounted on the lower back of a human participant for gait analysis.

Methods and Results

The acceleration data along three Cartesian coordinates is analyzed by the gait-force model to extract bio-mechanics information in both the dynamic state as in the gait analyzer and the steady state as in the balance scale. For the gait analyzer, the simple, noninvasive and versatile approach makes it appealing to a broad range of applications in clinical diagnosis, rehabilitation monitoring, athletic training, sport-apparel design, and many other areas. For the balance scale, it provides a portable platform to measure the postural deviation and the balance index under visual or vestibular sensory input conditions. Despite its simple construction and operation, excellent agreement has been demonstrated between its performance and the high-cost commercial balance unit over a wide dynamic range.

Conclusion

The portable balance scale is an ideal tool for routine monitoring of balance index, fall-risk assessment, and other balance-related health issues for both clinical and household use.

Balance evaluation techniques and physical therapy in post-stroke patients: A literature review

A stroke (cerebrovascular accident - CVA) is a significant social-economic issue. Approximately 15-30% of all patients develop life-long disability, 20% require over 3 months of specialized care in healthcare institutions, and the majority of the patients never recover the ability to maintain a proper vertical position. Such CVA sequelae as balance disturbances not only negatively affect patients' daily physical activity, but also result in social isolation. A number of standardized clinical scales, tests, and instrumental examination techniques have been proposed for evaluating not only post-CVA balance function, but also any changes in this function following various interventions. Even though scientific literature lists numerous methods and instruments for the improvement of balance after a CVA, not all of them are equally effective, and there have been rather controversial evaluations of some techniques. Nevertheless, the application of the majority of the techniques as complementary or alternative measures to traditional physical therapy (PT) frequently yields better results.

Sleep Assessment in Large Cohort Studies with High-Resolution Accelerometers

Accelerometry can be a practical replacement for polysomnography in large observational studies. This review discusses the need for sleep characterization in large observational studies, exemplified by the practices of the ongoing German National Cohort study. After brief descriptions of the physical principles and state-of-the-art accelerometer devices and an overview of public data analysis algorithms for sleep-wake differentiation, we demonstrate that the spectral properties of acceleration data provide additional features that can be exploited. This leads to a periodogram-based sleep detection algorithm. Finally, we address issues of data handling and quality assurance in large cohort studies.

Beyond the front end: Investigating a thigh worn accelerometer device for step count and bout detection in Parkinson's disease

Free-living ambulation with accelerometer-based devices is an attractive methodology to assess habitual behaviour within Parkinson's disease (PD). However, slowness of movement can contribute to difficulty in quantifying ambulatory/walking outcomes within this group by these devices. This study investigates the use of a commercial accelerometer device (activPAL^™) in those with moderate PD to understand its proprietary software (inbuilt algorithm) limitations. The values provided by the proprietary software are evaluated in comparison to novel algorithms on the same raw data to examine limitations for use within this cohort. The bespoke algorithms help to alter sensitivity in outcomes stemming from the same accelerometer data while also highlighting how slight changes in algorithms can drastically inflate/deflate values. In general, results show that the proprietary software generally quantifies lower values of outcomes (step and bout count), which is similar to previous findings. Variations in algorithm functionality highlight large heterogeneity in bout and step counts resulting from a lack of how they are defined within the literature. The novel alternative ambulatory algorithms presented here should be considered for use on raw data from the activPAL^™ in those with moderate/severe PD.

Wristband Accelerometers to motiVate arm Exercise after Stroke (WAVES): study protocol for a pilot randomized controlled trial

Background

Loss of upper limb function affects up to 85 % of acute stroke patients. Recovery of upper limb function requires regular intensive practise of specific upper limb tasks. To enhance intensity of practice interventions are being developed to encourage patients to undertake self-directed exercise practice. Most interventions do not translate well into everyday activities and stroke patients continue to find it difficult remembering integration of upper limb movements into daily activities. A wrist-worn device has been developed that monitors and provides ‘live’ upper limb activity feedback to remind patients to use their stroke arm in daily activities (The CueS wristband). The aim of this trial is to assess the feasibility of a multi-centre, observer blind, pilot randomised controlled trial of the CueS wristband in clinical stroke services.

Methods/design

This pilot randomised controlled feasibility trial aims to recruit 60 participants over 15 months from North East England. Participants will be within 3 months of stroke which has caused new reduced upper limb function and will still be receiving therapy. Each participant will be randomised to an intervention or control group. Intervention participants will wear a CueS wristband (between 8 am and 8 pm) providing “live” feedback towards pre-set movement goals through a simple visual display and vibration prompts whilst undertaking a 4-week upper limb therapy programme (reviewed twice weekly by an occupational/physiotherapist). Control participants will also complete the 4-week upper limb therapy programme but will wear a ‘sham’ CueS wristband that monitors upper limb activity but provides no feedback. Outcomes will determine study feasibility in terms of recruitment, retention, adverse events, adherence and collection of descriptive clinical and accelerometer motor performance data at baseline, 4 weeks and 8 weeks.

Discussion

The WAVES study will address an important gap in the evidence base by reporting the feasibility of undertaking an evaluation of emerging and affordable technology to encourage impaired upper limb activity after stroke. The study will establish whether the study protocol can be supported by clinical stroke services, thereby informing the design of a future multi-centre randomised controlled trial of clinical and cost-effectiveness.

Trial registration

ISRCTN:82306027. Registered 12 July 2016.

Electronic supplementary material

The online version of this article (doi:10.1186/s13063-016-1628-2) contains supplementary material, which is available to authorized users.

A smartphone approach for the 2 and 6-minute walk test

The 2 and 6-minute walk tests (2-6MWT) are used by rehabilitation professionals as a measure of exercise capacity. Our research has produced a new 2-6MWT BlackBerry smartphone application (app) that can be used to run the 2-6MWT and also provide new information about how the person moves during the test. The smartphone is worn on a belt at the lower back to record phone sensor data while walking. This data is used to identify foot strikes, calculate the total distance walked and step timing, and analyze pelvis accelerations. Information on symmetry, walking changes over time, and poor walking patterns is not available from a typical 2-6MWT and could help with clinical decision-making. The 2-6MWT app was evaluated in a pilot test using data from five able-bodied participants. Foot strike time was within 0.07 seconds when compared to gold standard video recordings. The total distance calculated by the app was within 1m of the measured distance.

Intensity Thresholds on Raw Acceleration Data: Euclidean Norm Minus One (ENMO) and Mean Amplitude Deviation (MAD) Approaches

Objectives

(1) To develop and internally-validate Euclidean Norm Minus One (ENMO) and Mean Amplitude Deviation (MAD) thresholds for separating sedentary behaviours from common light-intensity physical activities using raw acceleration data collected from both hip- and wrist-worn tri-axial accelerometers; and (2) to compare and evaluate the performances between the ENMO and MAD metrics.

Methods

Thirty-three adults [mean age (standard deviation (SD)) = 27.4 (5.9) years; mean BMI (SD) = 23.9 (3.7) kg/m²; 20 females (60.6%)] wore four accelerometers; an ActiGraph GT3X+ and a GENEActiv on the right hip; and an ActiGraph GT3X+ and a GENEActiv on the non-dominant wrist. Under laboratory-conditions, participants performed 16 different activities (11 sedentary behaviours and 5 light-intensity physical activities) for 5 minutes each. ENMO and MAD were computed from the raw acceleration data, and logistic regression and receiver-operating-characteristic (ROC) analyses were implemented to derive thresholds for activity discrimination. Areas under ROC curves (AUROC) were calculated to summarise performances and thresholds were assessed via executing leave-one-out-cross-validations.

Results

For both hip and wrist monitor placements, in comparison to the ActiGraph GT3X+ monitors, the ENMO and MAD values derived from the GENEActiv devices were observed to be slightly higher, particularly for the lower-intensity activities. Monitor-specific hip and wrist ENMO and MAD thresholds showed excellent ability for separating sedentary behaviours from motion-based light-intensity physical activities (in general, AUROCs >0.95), with validation indicating robustness. However, poor classification was experienced when attempting to isolate standing still from sedentary behaviours (in general, AUROCs <0.65). The ENMO and MAD metrics tended to perform similarly across activities and accelerometer brands.

Conclusions

Researchers can utilise these robust monitor-specific hip and wrist ENMO and MAD thresholds, in order to accurately separate sedentary behaviours from common motion-based light-intensity physical activities. However, caution should be taken if isolating sedentary behaviours from standing is of particular interest.

On the Orientation Error of IMU: Investigating Static and Dynamic Accuracy Targeting Human Motion

The accuracy in orientation tracking attainable by using inertial measurement units (IMU) when measuring human motion is still an open issue. This study presents a systematic quantification of the accuracy under static conditions and typical human dynamics, simulated by means of a robotic arm. Two sensor fusion algorithms, selected from the classes of the stochastic and complementary methods, are considered. The proposed protocol implements controlled and repeatable experimental conditions and validates accuracy for an extensive set of dynamic movements, that differ in frequency and amplitude of the movement. We found that dynamic performance of the tracking is only slightly dependent on the sensor fusion algorithm. Instead, it is dependent on the amplitude and frequency of the movement and a major contribution to the error derives from the orientation of the rotation axis w.r.t. the gravity vector. Absolute and relative errors upper bounds are found respectively in the range [0.7° ÷ 8.2°] and [1.0° ÷ 10.3°]. Alongside dynamic, static accuracy is thoroughly investigated, also with an emphasis on convergence behavior of the different algorithms. Reported results emphasize critical issues associated with the use of this technology and provide a baseline level of performance for the human motion related application.

A multi-resolution investigation for postural transition detection and quantification using a single wearable

BACKGROUND

Multi-resolution analyses involving wavelets are commonly applied to data derived from accelerometer-based wearable technologies (wearables) to identify and quantify postural transitions (PTs). Previous studies fail to provide rationale to inform their choice of wavelet and scale approximation when utilising discrete wavelet transforms. This study examines varying combinations of those parameters to identify best practice recommendations for detecting and quantifying sit-to-stand (SiSt) and stand-to-sit (StSi) PTs.

METHODS

39 young and 37 older participants completed three SiSt and StSi PTs on supported and unsupported chair types while wearing a single tri-axial accelerometer-based wearable on the lower back. Transition detection and duration were calculated through peak detection within the signal vector magnitude for a range of wavelets and scale approximations. A laboratory reference measure (2D video) was used for comparative analysis.

RESULTS

Detection accuracy of wavelet and scale combinations for the transitions was excellent for both SiSt (87-97%) and StSi (82-86%) PT-types. The duration of PTs derived from the wearable showed considerable bias and poor agreement compared with the reference videos. No differences were observed between chair types and age groups respectively.

CONCLUSIONS

Improved detection of PTs could be achieved through the incorporation of different wavelet and scale combinations for the assessment of specific PT types in clinical and free-living settings. An upper threshold of 5th scale approximations is advocated for improved detection of multiple PT-types. However, care should be taken estimating the duration of PTs using wearables.

Classification of physical activities based on body-segments coordination

Numerous innovations based on connected objects and physical activity (PA) monitoring have been proposed. However, recognition of PAs requires robust algorithm and methodology. The current study presents an innovative approach for PA recognition. It is based on the heuristic definition of postures and the use of body-segments coordination obtained through external sensors. The first part of this study presents the methodology required to define the set of accelerations which is the most appropriate to represent the particular body-segments coordination involved in the chosen PAs (here walking, running, and cycling). For that purpose, subjects of different ages and heterogeneous physical conditions walked, ran, cycled, and performed daily activities at different paces. From the 3D motion capture, vertical and horizontal accelerations of 8 anatomical landmarks representative of the body were computed. Then, the 680 combinations from up to 3 accelerations were compared to identify the most appropriate set of acceleration to discriminate the PAs in terms of body segment coordinations. The discrimination was based on the maximal Hausdorff Distance obtained between the different set of accelerations. The vertical accelerations of both knees demonstrated the best PAs discrimination. The second step was the proof of concept, implementing the proposed algorithm to classify PAs of new group of subjects. The originality of the proposed algorithm is the possibility to use the subject's specific measures as reference data. With the proposed algorithm, 94% of the trials were correctly classified. In conclusion, our study proposed a flexible and extendable methodology. At the current stage, the algorithm has been shown to be valid for heterogeneous subjects, which suggests that it could be deployed in clinical or health-related applications regardless of the subjects' physical abilities or characteristics.

Agreement of the activPAL3 and activPAL for characterising posture and stepping in adults and children

Characterisation of physical activity and sedentary behaviour under free-living conditions is becoming increasingly important in light of growing evidence for the health implications of these behaviours. The integrity of long-term follow-up and the ability to compare outcomes between studies is critically dependent on the agreement of outcomes from successive generations of monitors. This study evaluated the agreement of the activPAL and second generation activPAL3 devices. Agreement was assessed in both adults (median 27.6y IQR 22.6) (n=20) and young people (median 12.0y IQR 4.1) (n=8) during standardised and daily living (ADL) test activities. During standardised activities; sedentary duration, upright duration, stepping duration and overall number of steps were all detected within small limits of agreement (≤5%). However, the activPAL characterised more steps during jogging than the activPAL3 (adults +8.36%, young people +6.80%). Also during ADL differences arose due to different posture characterisation in young people and lower step detection in the activPAL than the activPAL3 (adults -20.58%, young people -11.43%). Second-by-second posture analysis demonstrated high levels (>90%) of agreement for all activities between monitors. However, sensitivity (68.7%) and positive predictive value (78.8%) for adult stepping demonstrated disagreement between monitor interpretation of movement patterns during ADL. Agreement between monitor outcomes for standardised activities provides confidence that these outcomes can be considered almost equivalent. However, for characterisation of jogging and smaller movements during ADL, it is likely that significant differences between monitor outcomes will arise.

Diagnosis and characterization of mania: Quantifying increased energy and activity in the human behavioral pattern monitor

Increased energy or activity is now an essential feature of the mania of Bipolar Disorder (BD) according to DSM-5. This study examined whether objective measures of increased energy can differentiate manic BD individuals and provide greater diagnostic accuracy compared to rating scales, extending the work of previous studies with smaller samples. We also tested the relationship between objective measures of energy and rating scales. 50 hospitalized manic BD patients were compared to healthy subjects (HCS, n=39) in the human Behavioral Pattern Monitor (hBPM) which quantifies motor activity and goal-directed behavior in an environment containing novel stimuli. Archival hBPM data from 17 schizophrenia patients were used in sensitivity and specificity analyses. Manic BD patients exhibited higher motor activity than HCS and higher novel object interactions. hBPM activity measures were not correlated with observer-rated symptoms, and hBPM activity was more sensitive in accurately classifying hospitalized BD subjects than observer ratings. Although the findings can only be generalized to inpatient populations, they suggest that increased energy, particularly specific and goal-directed exploration, is a distinguishing feature of BD mania and is best quantified by objective measures of motor activity. A better understanding is needed of the biological underpinnings of this cardinal feature.

Wearable inertial sensors for human movement analysis

INTRODUCTION

The present review aims to provide an overview of the most common uses of wearable inertial sensors in the field of clinical human movement analysis.

AREAS COVERED

Six main areas of application are analysed: gait analysis, stabilometry, instrumented clinical tests, upper body mobility assessment, daily-life activity monitoring and tremor assessment. Each area is analyzed both from a methodological and applicative point of view. The focus on the methodological approaches is meant to provide an idea of the computational complexity behind a variable/parameter/index of interest so that the reader is aware of the reliability of the approach. The focus on the application is meant to provide a practical guide for advising clinicians on how inertial sensors can help them in their clinical practice. Expert commentary: Less expensive and more easy to use than other systems used in human movement analysis, wearable sensors have evolved to the point that they can be considered ready for being part of routine clinical routine.

Parkinson's disease (PD) with dementia and falls is improved by AChEI? A preliminary study report

BACKGROUND/OBJECTIVE

Advanced PD is often associated with cognitive impairment and frequent falls. We describe a suggestive case report of PD associated with mild cognitive impairment (MCI) and falls. The aim of our study was to test alteration in balance potentially related to use of acetylcholinesterase inhibitor (AchEI). We address this hypothesis after keeping the patient in stable dosage of dopamine agonist.

METHODS/MEASUREMENTS

We describe an initial pharmacological management in a 78-year-old man affected by Parkinson disease (PD) associated with mild cognitive impairment (MCI) and history of falls. The diagnosis of PD was also confirmed by SPECT with DATSCAN, after CT-brain exclusion of potential other causes of the symptoms. Cognitive and motor performances of the patient were initially evaluated by Mini Mental Examination State Examination (MMSE), Short Physical Performance Battery (SPPB) and Romberg test. We also recorded gait parameters using an accelerometer, while balance and stability were assessed by stabilometric platform with open and closed eyes. We repeated cognitive and motor tests and gait and balance evaluation after stable dosage of dopamine agonist before and after introduction of AchEI.

RESULTS

After starting dopamine agonist therapy, there was a significant improvement in gait parameters (speed, stride/min, stride length, swing duration, and decrease in gait cycle duration and rolling duration). When stable dosage of dopamine agonist was reached, AchEI was introduced obtaining not only a significant improvement of cognitive performance, but also a significant positive change in balance.

CONCLUSION AND RELEVANCE

We hypothesize that AchEI could improve stability, balance and postural instability in addition to cognitive performance in PD with MCI and balance deficits.

Evaluation of suitability of a micro-processing unit of motion analysis for upper limb tracking

The aim of this study is to assess the suitability of a micro-processing unit of motion analysis (MPUMA), for monitoring, reproducing, and tracking upper limb movements. The MPUMA is based on an inertial measurement unit, a 16-bit digital signal controller and a customized algorithm. To validate the performance of the system, simultaneous recordings of the angular trajectory were performed with a video-based motion analysis system. A test of the flexo-extension of the shoulder joint during the active elevation in a complete range of 120º of the upper limb was carried out in 10 healthy volunteers. Additional tests were carried out to assess MPUMA performance during upper limb tracking. The first, a 3D motion reconstruction of three movements of the shoulder joint (flexo-extension, abduction-adduction, horizontal internal-external rotation), and the second, an upper limb tracking online during the execution of three movements of the shoulder joint followed by a continuous random movement without any restrictions by using a virtual model and a mechatronic device of the shoulder joint. Experimental results demonstrated that the MPUMA measured joint angles that are close to those from a motion-capture system with orientation RMS errors less than 3º.

Assessment of energy requirements in patients with short bowel syndrome by using the doubly labeled water method

BACKGROUND

Short bowel syndrome (SBS) is a serious malabsorption disorder, and dietetic management of patients with SBS is extremely challenging. Once the degree of undernutrition has been assessed, successful dietary intervention is contingent on an accurate estimation and provision of energy needs.

OBJECTIVE

We quantified total energy expenditure (TEE) in patients with SBS by using the doubly labeled water (DLW) method to inform energy needs and nutritional therapy goals.

DESIGN

In this observational study, TEE was measured in 22 participants, 11 with SBS and 11 sex-, age-, and body mass index (BMI)-matched controls (non-SBS), for 14 d with the DLW method. Predicted energy requirements were determined by using the Escott-Stump equation and compared with TEE determined with DLW. Resting energy expenditure was measured by using indirect calorimetry, and an accelerometer was also used to determine physical activity level.

RESULTS

Participants were aged (mean ± SD) 53 ± 8 y. Measured TEE was significantly higher than predicted TEE for the SBS group (1875 ± 276 compared with 1517 ± 175 kcal/d, P = 0.001) and also for the non-SBS group (2393 ± 445 compared with 1532 ± 178 kcal/d, P < 0.01). Measured TEE was significantly lower in the SBS group than in the non-SBS group (P < 0.01); however, predicted TEE did not differ significantly between the groups (P = 0.84). No significant differences were seen between measured and predicted resting energy expenditure either within or between groups.

CONCLUSIONS

Measured TEE in patients with SBS was significantly higher than predicted by using standard equations but also lower than values for age-, BMI-, and sex-matched non-SBS controls. Currently used formulas in clinical practice appear to underestimate energy requirements of patients with SBS, and revision is needed to prevent underfeeding and improve long-term prognosis. This trial was registered at clinicaltrials.gov as NCT02113228.

Amphetamine increases activity but not exploration in humans and mice

RATIONALE

Cross-species quantification of physiological behavior enables a better understanding of the biological systems underlying neuropsychiatric diseases such as bipolar disorder (BD). Cardinal symptoms of manic BD include increased motor activity and goal-directed behavior, thought to be related to increased catecholamine activity, potentially selective to dopamine homeostatic dysregulation.

OBJECTIVES

The objective of this study was to test whether acute administration of amphetamine, a norepinephrine/dopamine transporter inhibitor and dopamine releaser, would replicate the profile of activity and exploration observed in both humans with manic BD and mouse models of mania.

METHODS

Healthy volunteers with no psychiatric history were randomized to a one-time dose of placebo (n = 25), 10 mg d-amphetamine (n = 18), or 20 mg amphetamine (n = 23). Eighty mice were administered one of four doses of d-amphetamine or vehicle. Humans and mice were tested in the behavioral pattern monitor (BPM), which quantifies motor activity, exploratory behavior, and spatial patterns of behavior.

RESULTS

In humans, the 20-mg dose of amphetamine increased motor activity as measured by acceleration without marked effects on exploration or spatial patterns of activity. In mice, amphetamine increased activity, decreased specific exploration, and caused straighter, one-dimensional movements in a dose-dependent manner.

CONCLUSIONS

Consistent with mice, amphetamine increased motoric activity in humans without increasing exploration. Given that BD patients exhibit heightened exploration, these data further emphasize the limitation of amphetamine-induced hyperactivity as a suitable model for BD. Further, these studies highlight the utility of cross-species physiological paradigms in validating biological mechanisms of psychiatric diseases.

Inertial Sensor Technology for Elite Swimming Performance Analysis: A Systematic Review

Technical evaluation of swimming performance is an essential factor of elite athletic preparation. Novel methods of analysis, incorporating body worn inertial sensors (i.e., Microelectromechanical systems, or MEMS, accelerometers and gyroscopes), have received much attention recently from both research and commercial communities as an alternative to video-based approaches. This technology may allow for improved analysis of stroke mechanics, race performance and energy expenditure, as well as real-time feedback to the coach, potentially enabling more efficient, competitive and quantitative coaching. The aim of this paper is to provide a systematic review of the literature related to the use of inertial sensors for the technical analysis of swimming performance. This paper focuses on providing an evaluation of the accuracy of different feature detection algorithms described in the literature for the analysis of different phases of swimming, specifically starts, turns and free-swimming. The consequences associated with different sensor attachment locations are also considered for both single and multiple sensor configurations. Additional information such as this should help practitioners to select the most appropriate systems and methods for extracting the key performance related parameters that are important to them for analysing their swimmers' performance and may serve to inform both applied and research practices.

Childhood physical activity body contact risk: feasibility of a novel technique for objective measurements of impact speed, frequency, and intentionality

INTRODUCTION

Children at risk for bleeding injuries are restricted from body contact during physical activity but current recommendations are based on expert opinion.

AIM

Evaluate high-speed digital video recording as an objective measure of body contact risk during physical activity.

METHODS

Observational study of physical activities among healthy children, grouped according to participation in teams (vs. individual) and on their perceived risk of injury (high/low). High speed digital video recordings documented the collision target (floor/ground/ice, people, wall, equipment), estimated speed, and impact rates for team and individual activities, with and without expected body contact.

RESULTS

Among 348 participating children (3-16 years, 51% female), 32% to 78% experienced at least one contact. Impact type varied significantly (chi-square, p < 0.001) by activity category. Unstructured and Team high risk activity impacts were primarily with the floor/ground, whereas Individual low risk activities were characterized by equipment impacts. Impact speeds were typically 1.0 to 2.1 m s(-1) . Higher impact speeds occurred during instructional classes (2.1 m s(-1) ), unstructured free swim (1.9 m s(-1) ) and ball hockey (1.7 m s(-1) ). Impact rates were higher during Team high risk and Team low risk sports (3.0 and 1.8 impacts per minute, respectively) compared to Individual (high or low risk) or Unstructured activities (0.2-0.3 impacts per minute).

CONCLUSIONS

High speed video recordings of childhood physical activity are a feasible method for characterizing the frequency, type, direction and speed of impacts. Quantifying the impacts that occur during childhood physical activity could inform the guidelines for physical activity participation among children with identified bleeding risks.

Within trial validation and reliability of a single tri-axial accelerometer for gait assessment

Gait is a sensitive biomarker of decline in both cognitive and physical function. Therefore, the collection of gait data is an important feature of clinical assessments. Accelerometer-based body worn sensors are quickly becoming the preferred tool for assessing gait because they are small, useable in a wide variety of settings, offer more continuous spatio-temporal analysis and are inexpensive when compared with traditional gait assessment methodologies. The purpose of this study was to determine the validity and within test reliability of a low cost body worn movement sensor with associated algorithms to assess gait in a large group of older and younger healthy adults. We collected gait data over intermittent walks on an instrumented walkway for a within trial validation and also used the same accelerometer derived gait data for a within test reliability analysis. ICCs for validation and reliability were >0.756 and >0.965, respectively.

Comparison between a center of mass and a foot pressure sensor system for measuring gait parameters in healthy adults

[Purpose] The purpose of this study was to determine the relationship between an accelerometer system and a foot pressure sensor system for measuring gait characteristics during walking in healthy adults. [Subjects and Methods] Thirty-five healthy participants with no neurological, musculoskeletal, or cardiopulmonary disorders volunteered for this study. Gait characteristics were measured while participants walked freely along a 10-m walkway using two different measurement systems simultaneously. The first analysis system was based on center of mass using a wireless tri-axial accelerometer and the second system was a foot pressure sensor system. [Results] There was a significant and high correlation between the two systems with respect to gait velocity and cadence. The stride length as a percentage of the stride height measured with the center of mass system was significantly and highly correlated with stride length and stride velocity that was measured with the foot pressure system. Furthermore, stride length from the center of mass system was significantly and highly correlated with stride length and stride velocity from the foot pressure system. [Conclusion] A gait analysis based on a center of mass system is a valid method to assess the effectiveness of therapeutic interventions in a clinical setting.

Improvement of walking speed and gait symmetry in older patients after hip arthroplasty: a prospective cohort study

Background

Retraining walking in patients after hip or knee arthroplasty is an important component of rehabilitation especially in older persons whose social interactions are influenced by their level of mobility. The objective of this study was to test the effect of an intensive inpatient rehabilitation program on walking speed and gait symmetry in patients after hip arthroplasty (THA) using inertial sensor technology.

Methods

Twenty-nine patients undergoing a 4-week inpatient rehabilitation program following THA and 30 age-matched healthy subjects participated in this study. Walking speed and gait symmetry parameters were measured using inertial sensor device for standardized walking trials (2*20.3 m in a gym) at their self-selected normal and fast walking speeds on postoperative days 15, 21, and 27 in patients and in a single session in control subjects. Walking speed was measured using timing lights. Gait symmetry was determined using autocorrelation calculation of the cranio-caudal (CC) acceleration signals from an inertial sensor placed at the lower spine.

Results

Walking speed and gait symmetry improved from postoperative days 15–27 (speed, female: 3.2 and 4.5 m/s; male: 4.2 and 5.2 m/s; autocorrelation, female: 0.77 and 0.81; male: 0.70 and 0.79; P <0.001 for all). After the 4-week rehabilitation program, walking speed and gait symmetry were still lower than those in control subjects (speed, female 4.5 m/s vs. 5.7 m/s; male: 5.2 m/s vs. 5.3 m/s; autocorrelation, female: 0.81 vs. 0.88; male: 0.79 vs. 0.90; P <0.001 for all).

Conclusions

While patients with THA improved their walking capacity during a 4-week inpatient rehabilitation program, subsequent intensive gait training is warranted for achieving normal gait symmetry. Inertial sensor technology may be a useful tool for evaluating the rehabilitation process during the post-inpatient period.

Using Wearable Sensors and Machine Learning Models to Separate Functional Upper Extremity Use From Walking-Associated Arm Movements

OBJECTIVE

To improve measurement of upper extremity (UE) use in the community by evaluating the feasibility of using body-worn sensor data and machine learning models to distinguish productive prehensile and bimanual UE activity use from extraneous movements associated with walking.

DESIGN

Comparison of machine learning classification models with criterion standard of manually scored videos of performance in UE prosthesis users.

SETTING

Rehabilitation hospital training apartment.

PARTICIPANTS

Convenience sample of UE prosthesis users (n=5) and controls (n=13) similar in age and hand dominance (N=18).

INTERVENTIONS

Participants were filmed executing a series of functional activities; a trained observer annotated each frame to indicate either UE movement directed at functional activity or walking. Synchronized data from an inertial sensor attached to the dominant wrist were similarly classified as indicating either a functional use or walking. These data were used to train 3 classification models to predict the functional versus walking state given the associated sensor information. Models were trained over 4 trials: on UE amputees and controls and both within subject and across subject. Model performance was also examined with and without preprocessing (centering) in the across-subject trials.

MAIN OUTCOME MEASURE

Percent correct classification.

RESULTS

With the exception of the amputee/across-subject trial, at least 1 model classified >95% of test data correctly for all trial types. The top performer in the amputee/across-subject trial classified 85% of test examples correctly.

CONCLUSIONS

We have demonstrated that computationally lightweight classification models can use inertial data collected from wrist-worn sensors to reliably distinguish prosthetic UE movements during functional use from walking-associated movement. This approach has promise in objectively measuring real-world UE use of prosthetic limbs and may be helpful in clinical trials and in measuring response to treatment of other UE pathologies.

The Parkinsonian Gait Spatiotemporal Parameters Quantified by a Single Inertial Sensor before and after Automated Mechanical Peripheral Stimulation Treatment

This study aims to evaluate the change in gait spatiotemporal parameters in subjects with Parkinson's disease (PD) before and after Automated Mechanical Peripheral Stimulation (AMPS) treatment. Thirty-five subjects with PD and 35 healthy age-matched subjects took part in this study. A dedicated medical device (Gondola) was used to administer the AMPS. All patients with PD were treated in off levodopa phase and their gait performances were evaluated by an inertial measurement system before and after the intervention. The one-way ANOVA for repeated measures was performed to assess the differences between pre- and post-AMPS and the one-way ANOVA to assess the differences between PD patients and the control group. Spearman's correlations assessed the associations between patients with PD clinical status (H&Y) and the percentage of improvement of the gait variables after AMPS (α < 0.05 for all tests). The PD group had an improvement of 14.85% in the stride length; 14.77% in the gait velocity; and 29.91% in the gait propulsion. The correlation results showed that the higher the H&Y classification, the higher the stride length percentage of improvement. The treatment based on AMPS intervention seems to induce a better performance in the gait pattern of PD patients, mainly in intermediate and advanced stages of the condition.

A comparison of methods to detect postural transitions using a single tri-axial accelerometer

Two algorithms for evaluating postural transitions (PTs) in cohorts of 40 healthy younger and 40 older adults are described and evaluated. The time of sit-to-stand (SiSt) and stand-to-sit (StSi) transitions and their duration were measured with two tri-axial accelerometers, one on the chest and one on the lower back. Each algorithm was optimized for these sensor placements. The first algorithm for sensor placement on the chest used a scalar product and vertical velocity estimates. The second algorithm for sensor placement on the lower back used a vector magnitude and a discrete wavelet transform. Both algorithms performed excellently in PT classification for younger and older adults (>86%). However, the chest based sensor and algorithm were better for estimating transition duration (TD) with ICCs to video analysis ranging from 0.678 to 0.969.

Comparative assessment of different methods for the estimation of gait temporal parameters using a single inertial sensor: application to elderly, post-stroke, Parkinson's disease and Huntington's disease subjects

The estimation of gait temporal parameters with inertial measurement units (IMU) is a research topic of interest in clinical gait analysis. Several methods, based on the use of a single IMU mounted at waist level, have been proposed for the estimate of these parameters showing satisfactory performance when applied to the gait of healthy subjects. However, the above mentioned methods were developed and validated on healthy subjects and their applicability in pathological gait conditions was not systematically explored. We tested the three best performing methods found in a previous comparative study on data acquired from 10 older adults, 10 hemiparetic, 10 Parkinson's disease and 10 Huntington's disease subjects. An instrumented gait mat was used as gold standard. When pathological populations were analyzed, missed or extra events were found for all methods and a global decrease of their performance was observed to different extents depending on the specific group analyzed. The results revealed that none of the tested methods outperformed the others in terms of accuracy of the gait parameters determination for all the populations except the Parkinson's disease subjects group for which one of the methods performed better than others. The hemiparetic subjects group was the most critical group to analyze (stride duration errors between 4-5 % and step duration errors between 8-13 % of the actual values across methods). Only one method provides estimates of the stance and swing durations which however should be interpreted with caution in pathological populations (stance duration errors between 6-14 %, swing duration errors between 10-32 % of the actual values across populations).

Wearable pendant device monitoring using new wavelet-based methods shows daily life and laboratory gaits are different

Morbidity and falls are problematic for older people. Wearable devices are increasingly used to monitor daily activities. However, sensors often require rigid attachment to specific locations and shuffling or quiet standing may be confused with walking. Furthermore, it is unclear whether clinical gait assessments are correlated with how older people usually walk during daily life. Wavelet transformations of accelerometer and barometer data from a pendant device worn inside or outside clothing were used to identify walking (excluding shuffling or standing) by 51 older people (83 ± 4 years) during 25 min of 'free-living' activities. Accuracy was validated against annotated video. Training and testing were separated. Activities were only loosely structured including noisy data preceding pendant wearing. An electronic walkway was used for laboratory comparisons. Walking was classified (accuracy ≥97 %) with low false-positive errors (≤1.9%, κ ≥ 0.90). Median free-living cadence was lower than laboratory-assessed cadence (101 vs. 110 steps/min, p < 0.001) but correlated (r = 0.69). Free-living step time variability was significantly higher and uncorrelated with laboratory-assessed variability unless detrended. Remote gait impairment monitoring using wearable devices is feasible providing new ways to investigate morbidity and falls risk. Laboratory-assessed gait performances are correlated with free-living walks, but likely reflect the individual's 'best' performance.

Instrumented assessment of test battery for physical capability using an accelerometer: a feasibility study

Recent work has identified subdomains (tests) of physical capability that are recommended for assessment of the healthy ageing phenotype (HAP). These include: postural control, locomotion, endurance, repeated sit-to-stand-to-sit and TUG. Current assessment methods lack sensitivity and are error prone due to their lack of consistency and heterogeneity of reported outcomes; instrumentation with body worn monitors provides a method to address these potential weaknesses. This work proposes the use of a single tri-axial accelerometer-based device with appropriate algorithms (referred to here as a body worn monitor, BWM) for the purposes of instrumented testing during physicality capability assessment. In this pilot study we present 14 BWM-based outcomes across the subdomains which include magnitude, frequency and spatio-temporal characteristics. Where possible, we compared BWM outcomes with manually recorded values and found no significant differences between locomotion and TUG tasks (p ≥ 0.319). Significant differences were found for the total distance walked during endurance (p = 0.037) and times for repeated sit-to-stand-to-sit transitions (p < 0.000). We identified reasons for differences and make recommendations for future testing. We were also able to quantify additional characteristics of postural control and gait which could be sensitive outcomes for future HAP assessment. Our findings demonstrate the feasibility of this method to enhance measurement of physical capacity. The methodology can also be applied to a wide variety of accelerometer-based monitors and is applicable to a range of intervention-based studies or pathological assessment.

Instrumenting gait with an accelerometer: a system and algorithm examination

Gait is an important clinical assessment tool since changes in gait may reflect changes in general health. Measurement of gait is a complex process which has been restricted to the laboratory until relatively recently. The application of an inexpensive body worn sensor with appropriate gait algorithms (BWM) is an attractive alternative and offers the potential to assess gait in any setting. In this study we investigated the use of a low-cost BWM, compared to laboratory reference using a robust testing protocol in both younger and older adults. We observed that the BWM is a valid tool for estimating total step count and mean spatio-temporal gait characteristics however agreement for variability and asymmetry results was poor. We conducted a detailed investigation to explain the poor agreement between systems and determined it was due to inherent differences between the systems rather than inability of the sensor to measure the gait characteristics. The results highlight caution in the choice of reference system for validation studies. The BWM used in this study has the potential to gather longitudinal (real-world) spatio-temporal gait data that could be readily used in large lifestyle-based intervention studies, but further refinement of the algorithm(s) is required.

Wearable sensor-based rehabilitation exercise assessment for knee osteoarthritis

Since the knee joint bears the full weight load of the human body and the highest pressure loads while providing flexible movement, it is the body part most vulnerable and susceptible to osteoarthritis. In exercise therapy, the early rehabilitation stages last for approximately six weeks, during which the patient works with the physical therapist several times each week. The patient is afterwards given instructions for continuing rehabilitation exercise by him/herself at home. This study develops a rehabilitation exercise assessment mechanism using three wearable sensors mounted on the chest, thigh and shank of the working leg in order to enable the patients with knee osteoarthritis to manage their own rehabilitation progress. In this work, time-domain, frequency-domain features and angle information of the motion sensor signals are used to classify the exercise type and identify whether their postures are proper or not. Three types of rehabilitation exercise commonly prescribed to knee osteoarthritis patients are: Short-Arc Exercise, Straight Leg Raise, and Quadriceps Strengthening Mini-squats. After ten subjects performed the three kinds of rehabilitation activities, three validation techniques including 10-fold cross-validation, within subject cross validation, and leave-one-subject cross validation are utilized to confirm the proposed mechanism. The overall recognition accuracy for exercise type classification is 97.29% and for exercise posture identification it is 88.26%. The experimental results demonstrate the feasibility of the proposed mechanism which can help patients perform rehabilitation movements and progress effectively. Moreover, the proposed mechanism is able to detect multiple errors at once, fulfilling the requirements for rehabilitation assessment.

The development and validation of an activity monitoring system for use in measurement of posture of childbearing women during first stage of labor

INTRODUCTION

There is limited understanding of the type and extent of maternal postures that midwives should encourage or support during labor. The aims of this study were to identify a set of postures and movements commonly seen during labor, to develop an activity monitoring system for use during labor, and to validate this system design.

METHODS

Volunteer student midwives simulated maternal activity during labor in a laboratory setting. Participants (N = 15) wore monitors adhered to the left thigh and left shank, and adopted 13 common postures of laboring women for 3 minutes each. Simulated activities were recorded using a video camera. Postures and movements were coded from the video, and statistical analysis conducted of agreement between coded video data and outputs of the activity monitoring system.

RESULTS

Excellent agreement between the 2 raters of the video recordings was found (Cohen's κ = 0.95). Both sensitivity and specificity of the activity monitoring system were greater than 80% for standing, lying, kneeling, and sitting (legs dangling).

DISCUSSION

This validated system can be used to measure elected activity of laboring women and report on effects of postures on length of first stage, pain experience, birth satisfaction, and neonatal condition. This validated maternal posture-monitoring system is available as a reference-and for use by researchers who wish to develop research in this area.