Direct measurement of human movement by accelerometry

Classification of Behaviour in Conventional and Slow-Growing Strains of Broiler Chickens Using Tri-Axial Accelerometers

Behavioural states such as walking, sitting and standing are important in indicating welfare, including lameness in broiler chickens. However, manual behavioural observations of individuals are often limited by time constraints and small sample sizes. Three-dimensional accelerometers have the potential to collect information on animal behaviour. We applied a random forest algorithm to process accelerometer data from broiler chickens. Data from three broiler strains at a range of ages (from 25 to 49 days old) were used to train and test the algorithm, and unlike other studies, the algorithm was further tested on an unseen broiler strain. When tested on unseen birds from the three training broiler strains, the random forest model classified behaviours with very good accuracy (92%) and specificity (94%) and good sensitivity (88%) and precision (88%). With the new, unseen strain, the model classified behaviours with very good accuracy (94%), sensitivity (91%), specificity (96%) and precision (91%). We therefore successfully used a random forest model to automatically detect three broiler behaviours across four different strains and different ages using accelerometers. These findings demonstrated that accelerometers can be used to automatically record behaviours to supplement biomechanical and behavioural research and support in the reduction principle of the 3Rs.

Design, synthesis, in-silico studies and apoptotic activity of novel amide enriched 2-(1H)- quinazolinone derivatives

Cancer is a broad classification of diseases that can affect any organ or body tissue due to aberrant cellular proliferation for unknown reasons. Many present chemotherapeutic drugs are highly toxic and have little selectivity. Additionally, they lead to the development of medication resistance. Therefore, developing tailored chemotherapeutic drugs with minimal side effects and good selectivity is crucial for cancer treatment. 2-(1H)-Quinazolinone is one of the vital scaffold and anticancer activity is one of the prominent biological activities of this class. Here we report the novel set of amide-enriched 2-(1H)-quinazolinone derivatives (7a-j) and their apoptotic activity with the help of MTT assay method against four human cancer cell lines: PC3 (prostate cancer), DU-145 (prostate cancer), A549 (lung cancer), and MCF7 (breast cancer). When compared to etoposide, every synthetic test compound (7a-j) exhibited moderate to excellent activity. The IC50 values of the new amide derivatives (7a-j) varied from 0.07 ± 0.0061 μM to 10.8 ± 0.69 μM. While the positive control, etoposide, exhibited 1.97 ± 0.45 μM to 3.08 ± 0.135 μM range. Among the novel amide derivatives (7a-j), in particular, 7i and 7j showed strong apoptotic activity against MCF7; 7h showed against PC3, and 7g showed against DU-145. Molecular docking studies of test compounds (7a-j) with the EGFR tyrosine kinase domain (PDB ID: 1M17) protein provided the significant docking scores for each test compound (7a-j) (-9.00 to -9.67 kcal/mol). Additionally, DFT investigations and MD simulations validated the predictions of molecular docking. According to the findings of the ADME analysis, oral absorption by humans is anticipated to be higher than 85 % for all test compounds.

Enhancing spatial navigation skills in mild cognitive impairment patients: a usability study of a new version of ANTaging software

Introduction

Mild Cognitive Impairment (MCI) often presents challenges related to spatial navigation and retention of spatial information. Navigating space involves intricate integration of bodily and environmental cues. Spatial memory is dependent on two distinct frame of reference systems for organizing this information: egocentric and allocentric frames of reference. Virtual Reality (VR) has emerged as a promising technology for enhancing spatial navigation skills and spatial memory by facilitating the manipulation of bodily, environmental, and cognitive cues.

Methods

This usability study was based on a fully within-subjects design in which seven MCI patients underwent two kinds of VR conditions: participants were required to complete the ANTaging demo both in Oculus Rift S (immersive condition) and in Samsung UHD 4K monitor (semi-immersive condition). Participants were seated and they had to use a foot-motion pad to navigate and explore the environment to collect and relocate some objects in the virtual environment. Post-interaction, users provided feedback on their experiences. Additionally, usability, potential side effects, data analysis feasibility, and user preferences with immersive and semi-immersive technologies were assessed through questionnaires.

Results

Results indicated higher usability ratings for the semi-immersive setup, with fewer negative effects reported compared to the immersive counterpart. According to qualitative analyses of the interviews, patients do seem to like both VR apparatuses even though the semi-immersive condition was perceived as the most suitable choice because of the size of the screen. Patients generally found it difficult to remember object locations. Participants expressed the need for more practice with the foot-motion pad, despite an overall positive experience. They generally would like to use this system to improve their memory.

Discussion

Identifying these key aspects was crucial for refining the system before the upcoming clinical trial. This study sheds light on the potential of semi-immersive VR in aiding individuals with MCI, paving the way for enhanced spatial navigation interventions.

Reliability and validity of estimated angles information assessed using inertial measurement unit-based motion sensors

BACKGROUND

Inertial measurement unit (IMU)-based motion sensors are affordable, and their use is appropriate for rehabilitation. However, regarding the accuracy of estimated angle information obtained from this sensor, it is reported that it is likely affected by velocity.

OBJECTIVE

The present study investigated the reliability and validity of the angle information obtained using IMU-based sensors compared with a three-dimensional (3D) motion analyzer.

METHODS

The Euler angle obtained using the 3D motion analyzer and the angle obtained using the IMU-based sensor (IMU angle) were compared. Reliability was assessed by comparing the Bland-Altman analysis, intra-class correlation coefficient (ICC) (1,1), and cross-correlation function. The root mean square (RMS) error, ICC (2,1), and cross-correlation function were used to compare data on the Euler and IMU angles to evaluate the validity.

RESULTS

Regarding reliability, the Bland-Atman analysis indicated no fixed or proportional bias in the angle measurements. The measurement errors ranged from 0.2° to 3.2°. In the validity, the RMS error ranged from 0.3° to 2.2°. The ICCs (2,1) were 0.9. The cross-correlation functions were >0.9, which indicated a high degree of agreement.

CONCLUSION

The IMU-based sensor had a high reliability and validity. The IMU angle may be used in rehabilitation.

Design and validation of depth camera-based static posture assessment system

Postural abnormalities have become a prevalent issue affecting individuals of all ages, resulting in a diminished quality of life. Easy-use and reliable posture assessment tools can aid in screening for and correcting posture deviation at an early stage. In this study, we present a depth camera-based static posture assessment system to screen for common postural anomalies such as uneven shoulders, pelvic tilt, bowlegs and knock-knees, forward head, scoliosis, and shoulder blade inclination. The system consists of an Azure Kinect camera, a laptop, and evaluation software. Our system accurately measures skeleton and posture indexes and shows favorable agreement with a golden standard optical infrared motion capture system. The findings indicate that the system is a low-cost posture assessment tool with high precision and accuracy, suitable for initial screening of postural abnormalities in individuals of all ages.

Efficacy of a Commercial Physical Activity Monitor in Longitudinal Tracking of Patients With Pulmonary Hypertension: A Pilot Study

BACKGROUND

Patients with pulmonary arterial hypertension have quality-of-life limitations, decreased exercise capacity, and poor prognosis if the condition is left untreated. Standard exercise testing is routinely performed to evaluate patients with pulmonary arterial hypertension but may be limited in its ability to monitor activity levels in daily living.

OBJECTIVE

To evaluate the validity of the commercial Fitbit Charge HR as a tool to assess real-time exercise capacity as compared with standard exercise testing.

METHODS

Ambulatory pediatric and adult patients were enrolled and given a Fitbit with instructions to continuously wear the device during waking hours. Patients underwent a 6-minute walk test, cardiopulmonary exercise test, and a 36-Item Short Form Health Survey on the day of enrollment and follow-up. Twenty-seven ambulatory patients with pulmonary arterial hypertension were enrolled, and 21 had sufficient data for analyses (median age, 25 years [range, 13-59 years]; 14 female participants).

RESULTS

Daily steps measured by the Fitbit had a positive correlation with 6-minute walk distance (r = 0.72, P = .03) and an inverse trend with World Health Organization functional class. On the 36-Item Short Form Health Survey, 77% of patients reported improvement in vitality (P = .055). At follow-up, there was a strong correlation between number of steps recorded by Fitbit and role limitations because of physical problems (r = 0.88, P = .02) and weaker correlations with other quality-of-life markers.

CONCLUSION

The findings of this pilot study suggest activity monitors may have potential as a simple and novel method of assessing longitudinal exercise capacity and activity levels in patients with pulmonary hypertension. Further study in larger cohorts of patients is warranted to determine which accelerometer measures correlate best with outcomes.

Performance of multiple neural networks in predicting lower limb joint moments using wearable sensors

Joint moment measurements represent an objective biomechemical parameter in joint health assessment. Inverse dynamics based on 3D motion capture data is the current 'gold standard' to estimate joint moments. Recently, machine learning combined with data measured by wearable technologies such electromyography (EMG), inertial measurement units (IMU), and electrogoniometers (GON) has been used to enable fast, easy, and low-cost measurements of joint moments. This study investigates the ability of various deep neural networks to predict lower limb joint moments merely from IMU sensors. The performance of five different deep neural networks (InceptionTimePlus, eXplainable convolutional neural network (XCM), XCMplus, Recurrent neural network (RNNplus), and Time Series Transformer (TSTPlus)) were tested to predict hip, knee, ankle, and subtalar moments using acceleration and gyroscope measurements of four IMU sensors at the trunk, thigh, shank, and foot. Multiple locomotion modes were considered including level-ground walking, treadmill walking, stair ascent, stair descent, ramp ascent, and ramp descent. We show that XCM can accurately predict lower limb joint moments using data of only four IMUs with RMSE of 0.046 ± 0.013 Nm/kg compared to 0.064 ± 0.003 Nm/kg on average for the other architectures. We found that hip, knee, and ankle joint moments predictions had a comparable RMSE with an average of 0.069 Nm/kg, while subtalar joint moments had the lowest RMSE of 0.033 Nm/kg. The real-time feedback that can be derived from the proposed method can be highly valuable for sports scientists and physiotherapists to gain insights into biomechanics, technique, and form to develop personalized training and rehabilitation programs.

[Motion analysis systems in research and for practicing orthopedists]

BACKGROUND

Complex biomechanical motion analysis can provide relevant information for a variety of orthopedic problems. When purchasing motion analysis systems, in addition to the classical measurement quality criteria (validity, reliability, objectivity), spatial and temporal conditions, as well as the requirements for the qualification of the measuring personnel should be considered.

APPLICATION

In complex movement analysis, systems are used to determine kinematics, kinetics and muscle activity (electromyography). This article gives an overview of methods of complex biomechanical motion analysis for use in orthopaedic research or for individual patient care. In addition to the use for pure movement analysis, the use of movement analysis methods in the field of biofeedback training is discussed.

ACQUISITION

For the specific acquisition of motion analysis systems, it is recommended to contact professional societies (e.g., the German Society for Biomechanics),universities with existing motion analysis facilities or distributors in the field of biomechanics.

Angle measurement stability and cycle counting accuracy of hours-long duration IMU based arm motion tracking with application to normal shoulder ADLs

BACKGROUND

Inertial measurement units are increasing used for monitoring joint motion, but there is a need to demonstrate their suitability during hours-long continuous use, as well as a need for validated methods to count arm cycles and provide descriptions of typical cycles.

RESEARCH QUESTION

Do IMU sensors and rainflow counting have sufficient accuracy for tracking and cycle counting of hours-long continuous arm motion? If so, what are the cycle rates of normal arm ADL and is there a representative cycle that can serve as a 'gait cycle' for the arm?

METHODS

IMU sensors continuously tracked a robot performing 8 h of simulated cyclic arm motion. Error in the angle measurements was regressed against time to determine the rate of error and the total accumulated error. Additionally, the cycle count accuracy of rainflow, peak/valley, and Fourier transform counting methods was evaluated.

RESULTS

Over 8 h the IMU measurements accumulated a maximum 0.473° of error and the rainflow method counted cycles with less than 1% error. Applying rainflow counting to normal shoulder ADL resulted in an average rate of 533 elevation cycles per day.Tabulating the ADL cycles by mean and range values into a matrix and calculating the centroid, the single best values representing arm elevation cycles were a mean of 22.4° and a range of 21.6°.

SIGNIFICANCE

IMU sensors can track arm motion for 8 h with little increase in error, though during longer durations error may reach unacceptable levels. For normal arm ADL, the rainflow determined count of arm elevation full-cycles differed from previous estimates based on peak/valley counting. From the rainflow counting, a single cycle representation of cycle mean and range was determined that can be used as a 'gait cycle' for the shoulder.

Optimization of an Impact-Based Frequency Up-Converted Piezoelectric Vibration Energy Harvester for Wearable Devices

This work presents a novel development of the impact-based mechanism for piezoelectric vibration energy harvesters. More precisely, the effect of an impacting mass on a cantilever piezoelectric transducer is studied both in terms of the tip mass value attached to the cantilever and impact position to find an optimal condition for power extraction. At first, the study is carried out by means of parametric analyses at varying tip mass and impact position on a unimorph MEMS cantilever, and a suitable physical interpretation of the associated electromechanical response is given. The effect of multiple impacts is also considered. From the analysis, it emerges that the most effective configuration, in terms of power output, is an impact at the cantilever tip without a tip mass. By changing the value of the tip mass, a sub-optimal impact position along the beam axis can also be identified. Moreover, the effect of a tip mass is deleterious on the power performance, contrary to the well-known case of a resonant energy harvester. A mesoscale prototype with a bimorph transducer is fabricated and tested to validate the computational models. The comparison shows a good agreement between numerical models and the experiments. The proposed approach is promising in the field of consumer electronics, such as wearable devices, in which the impact-based device moves at the frequencies of human movement and is much lower than those of microsystems.

Enhancing Free-Living Fall Risk Assessment: Contextualizing Mobility Based IMU Data

Fall risk assessment needs contemporary approaches based on habitual data. Currently, inertial measurement unit (IMU)-based wearables are used to inform free-living spatio-temporal gait characteristics to inform mobility assessment. Typically, a fluctuation of those characteristics will infer an increased fall risk. However, current approaches with IMUs alone remain limited, as there are no contextual data to comprehensively determine if underlying mechanistic (intrinsic) or environmental (extrinsic) factors impact mobility and, therefore, fall risk. Here, a case study is used to explore and discuss how contemporary video-based wearables could be used to supplement arising mobility-based IMU gait data to better inform habitual fall risk assessment. A single stroke survivor was recruited, and he conducted a series of mobility tasks in a lab and beyond while wearing video-based glasses and a single IMU. The latter generated topical gait characteristics that were discussed according to current research practices. Although current IMU-based approaches are beginning to provide habitual data, they remain limited. Given the plethora of extrinsic factors that may influence mobility-based gait, there is a need to corroborate IMUs with video data to comprehensively inform fall risk assessment. Use of artificial intelligence (AI)-based computer vision approaches could drastically aid the processing of video data in a timely and ethical manner. Many off-the-shelf AI tools exist to aid this current need and provide a means to automate contextual analysis to better inform mobility from IMU gait data for an individualized and contemporary approach to habitual fall risk assessment.

Cultural adaptation and validation of the "Pregnancy Physical Activity Questionnaire" for the Portuguese population

BACKGROUND

The lack of instruments to assess the level of physical activity in pregnant women, led to the development of the PPAQ (Pregnancy Physical Activity Questionnaire), a self-administered questionnaire, which has already been translated in several countries and has already been used in several studies.

AIM(S)

Translate and adapt the PPAQ into Portuguese and test its reliability and validity.

METHODS

An analytical observational study was carried out. Linguistic and semantic equivalence was performed through translation and back-translation and content validity was tested by a panel of experts. To test reliability, a test-retest was performed on a sample of 184 pregnant women, with an interval of 7 days and the ICC was used. To test the criterion validity, Pearson's correlation coefficient (r) was used between the PPAQ and the accelerometer, in a sample of 226 pregnant women.

FINDINGS

The questionnaire was considered comprehensive. The ICC values of Reliability were: total score (0.77); sedentary activities (0.87); light-intensity activities (0.76); moderate-intensity activities (0.76); vigorous-intensity activities (0.70). For criterion validity was obtained a coefficient correlation of r = -0.030, considered weak and negative, for total activity.

DISCUSSION

This study describes the translation and validation process of the PPAQ questionnaire from English to Portuguese. The final version of the PPAQ was considered as a valid instrument in terms of content to measure physical activity and was referred to as being simple to apply and easy to understand.

CONCLUSION

The PPAQ has content validity, excellent reliability and weak criterion validity, as in the original version.

Steps parameters of elderly patients hospitalised for an acute medical illness in a Swiss University Hospital: a monocentric observational pilot-study

BACKGROUND

Objective mobility goals for elderly hospitalised medical patients remain debated. We therefore studied steps parameters of elderly patients hospitalised for an acute illness, to determine goals for future interventional trials and medical practice.

METHODS

Observational study conducted from February to November 2018 in a medical ward of the Lausanne University Hospital, Switzerland. We measured the step parameters of consecutive medical patients aged ≥65 years admitted for an acute medical illness using a wrist accelerometer (Geneactiv). We also collected demographic, somatic and functional factors.

RESULTS

Overall, 187 inpatients had their step parameters (daily step count, walking cadence and bout duration) measured with accelerometers worn for a mean of 3.6 days (standard deviation [SD] 3.2). Elderly inpatients (81.5 years, SD 8.5) walked a median of 603 steps daily (interquartile range [IQR] 456-809), at a median cadence of 100 steps/minute (IQR 99-101) with median walking bouts of 33 seconds (IQR 27-37) and with 70% of the walking bouts lasting less than 30 seconds. Patients walking ≥600 steps were younger (80.4 years, SD 8.9 vs 82.8 years,SD 7.9, p = 0.050) and had a longer length of stay (7.8 days, SD 5.1 vs 6.1 days, SD 4.1, p = 0.011) than those walking <600 steps. Patients at high risk of bed sores walked less (564 steps, IQR 394-814 vs 626, IQR 526-840) than those with a lower risk of sores.

CONCLUSION

During a hospitalisation for an acute medical illness, patients aged ≥65 years walk a mere 603 steps daily and most of the time for periods of less than 30 seconds. This information should be used to build up future interventional trials or to set mobility goals for patients hospitalised in Swiss hospitals.

Association of Food Intake with Sleep Durations in Adolescents from a Capital City in Northeastern Brazil

(1) Background: During adolescence, there are significant changes in food consumption, such as reducing the consumption of in natura or minimally processed foods and increasing the consumption of ultra-processed foods. Thus, eating habits can influence sleep duration and, consequently, affect the quality of life of young people. This study thus aims to estimate the association of consumption of in natura or minimally processed, processed, and ultra-processed foods with sleep durations in adolescents. (2) Methods: This is a cross-sectional study including 964 adolescents (18 to 19 years old) from the 1997 to 1998 birth cohort in São Luís, Maranhão. Food consumption was assessed using the food frequency questionnaire (FFQ) and stratified based on the NOVA classification. Sleep duration was verified using accelerometry in hours. The analysis of the association between the consumption of in natura or minimally processed, processedand ultra-processed foods with sleep durations in adolescents used crude and adjusted linear regression (by gender, age, skin color, education, economic class, work, consumption of alcohol, smoking, screen time, physical activity, use of illicit drugs, anxiety, depressive symptoms, and lean and fat mass). A directed acyclic graph (DAG) was used to determine the minimum set of adjustment factors. (3) Results: Of the 964 individuals evaluated, 52.0% were female. The mean sleep duration was 6 h (± 0.95). In the crude and adjusted analyses, no association was observed between food consumption according to the degree of processing and adolescent sleep durations. (4) Conclusion: There was no association between the consumption of in natura or minimally processed, processed, and ultra-processed foods with sleep durations.

Longitudinal Trend Monitoring of Multiple Sclerosis Ambulation Using Smartphones

Goal: Smartphone and wearable devices may act as powerful tools to remotely monitor physical function in people with neurodegenerative and autoimmune diseases from out-of-clinic environments. Detection of progression onset or worsening of symptoms is especially important in people living with multiple sclerosis (PwMS) in order to enable optimally adapted therapeutic strategies. MS symptoms typically follow subtle and fluctuating disease courses, patient-to-patient, and over time. Current in-clinic assessments are often too infrequently administered to reflect longitudinal changes in MS impairment that impact daily life. This work, therefore, explores how smartphones can administer daily two-minute walking assessments to monitor PwMS physical function at home. Methods: Remotely collected smartphone inertial sensor data was transformed through state-of-the-art Deep Convolutional Neural Networks, to estimate a participant's daily ambulatory-related disease severity, longitudinally over a 24-week study. Results: This study demonstrated that smartphone-based ambulatory severity outcomes could accurately estimate MS level of disability, as measured by the EDSS score ([Formula: see text]: 0.56,[Formula: see text]0.001). Furthermore, longitudinal severity outcomes were shown to accurately reflect individual participants' level of disability over the study duration. Conclusion: Smartphone-based assessments, that can be performed by patients from their home environments, could greatly augment standard in-clinic outcomes for neurodegenerative diseases. The ability to understand the impact of disease on daily-life between clinical visits, through objective digital outcomes, paves the way forward to better measure and identify signs of disease progression that may be occurring out-of-clinic, to monitor how different patients respond to various treatments, and to ultimately enable the development of better, and more personalised care.

Accelerometer measures in extremely preterm and or extremely low birth weight infants and association with abnormal general movements assessments at 28- and 32-weeks postmenstrual age

BACKGROUND

Assessment of general movements (GMs) in preterm infants is qualitative and potentially subjective. Accelerometers provide quantitative data that could overcome the problems of the GMs assessment.

STUDY AIMS

To determine if quantitative measures (obtained from four tri-axial accelerometers) correlate with GMs assessments performed in the preterm period at 28- or 32-weeks postmenstrual age (PMA).

STUDY DESIGN

Prospective observational study. Tri-axial accelerometers were applied to the dorsum of each hand and foot at 28- and 32-weeks PMA. Simultaneous video recordings of the babies' spontaneous movements were made to assess GMs.

SUBJECTS

Eligible babies were born <28-weeks PMA or had a birth weight of <1000 g. Babies were recruited before they reached 33-weeks PMA.

OUTCOME MEASURES

GMs assessments were made offline on the video recordings. Forty-six quantitative motor parameters were calculated during the same periods of activity and compared with GMs assessments.

RESULTS

At 28-weeks PMA, 24/43 (55.8 %) babies had abnormal GMs. At 32-weeks PMA, 26/57 (45.6 %) had abnormal GMs. The inter-rater reliability of the GMs was poor. When comparing MDS measures between; infants with normal and those with abnormal GMs, at 28-weeks PMA, 7/46 parameters were significantly different, and at 32-weeks PMA, 19/46 parameters were significantly different.

CONCLUSION

Isolated use of quantitative movement measures, obtained from four tri-axial accelerometers before hospital discharge, correlate with the GMs assessments at both 28-weeks and 32-weeks PMA. Accelerometers may provide a useful screening tool for abnormal GMs in preterm infants and could overcome issues with inter-rater reliability.

Significant Features for Human Activity Recognition Using Tri-Axial Accelerometers

Activity recognition using wearable sensors has become essential for a variety of applications. Tri-axial accelerometers are the most widely used sensor for activity recognition. Although various features have been used to capture patterns and classify the accelerometer signals to recognise activities, there is no consensus on the best features to choose. Reducing the number of features can reduce the computational cost and complexity and enhance the performance of the classifiers. This paper identifies the signal features that have significant discriminative power between different human activities. It also investigates the effect of sensor placement location, the sampling frequency, and activity complexity on the selected features. A comprehensive list of 193 signal features has been extracted from accelerometer signals of four publicly available datasets, including features that have never been used before for activity recognition. Feature significance was measured using the Joint Mutual Information Maximisation (JMIM) method. Common significant features among all the datasets were identified. The results show that the sensor placement location does not significantly affect recognition performance, nor does it affect the significant sub-set of features. The results also showed that with high sampling frequency, features related to signal repeatability and regularity show high discriminative power.

The relationship between novelty-seeking traits and behavior: Establishing construct validity for the human Behavioral Pattern Monitor

Novelty seeking is a tendency to approach new situations, putatively driven by the brain's catecholaminergic system. It is traditionally measured via self-report, but a laboratory-based paradigm, the human Behavioral Pattern Monitor (hBPM), quantifies behavior in a novel environment and has utility in cross-species studies of neuropsychiatric disorders. Our primary aim assessed whether self-reported novelty-seeking traits were associated with novelty-seeking behavior in the hBPM. An existing sample of 106 volunteers were categorized as high vs. low novelty seekers using the Temperament and Character Inventory (TCI). Subjects had been randomized to one dose of amphetamine (10 or 20 mg) or modafinil (200 or 400 mg), allowing us to explore whether a pharmacological catecholamine challenge further enhanced novelty-seeking behavior. High TCI novelty-seekers had more hBPM motor activity and novel object interactions. The exploratory analyses, although limited by low power, suggested that amphetamine and modafinil did not markedly moderate novelty-seeking traits. The hBPM demonstrates construct validity as a lab-based measure of novelty seeking and thus useful in translational studies of neuropsychiatric conditions and treatment options. Further research may illuminate whether a biological predisposition towards higher catecholaminergic activity, combined with the novelty-seeking trait, may increase propensity for risky and addictive behaviors.

Kinect-Based Assessment of Lower Limbs during Gait in Post-Stroke Hemiplegic Patients: A Narrative Review

The aim of this review was to present an overview of the state of the art in the use of the Microsoft Kinect camera to assess gait in post-stroke individuals through an analysis of the available literature. In recent years, several studies have explored the potentiality, accuracy, and effectiveness of this 3D optical sensor as an easy-to-use and non-invasive clinical measurement tool for the assessment of gait parameters in several pathologies. Focusing on stroke individuals, some of the available studies aimed to directly assess and characterize their gait patterns. In contrast, other studies focused on the validation of Kinect-based measurements with respect to a gold-standard reference (i.e., optoelectronic systems). However, the nonhomogeneous characteristics of the participants, of the measures, of the methodologies, and of the purposes of the studies make it difficult to adequately compare the results. This leads to uncertainties about the strengths and weaknesses of this technology in this pathological state. The final purpose of this narrative review was to describe and summarize the main features of the available works on gait in the post-stroke population, highlighting similarities and differences in the methodological approach and primary findings, thus facilitating comparisons of the studies as much as possible.

Learning from Acceleration Data to Differentiate the Posture, Dynamic and Static Work of the Back: An Experimental Setup

Information on body posture, postural change, and dynamic and static work is essential in understanding biomechanical exposure and has many applications in ergonomics and healthcare. This study aimed at evaluating the possibility of using triaxial acceleration data to classify postures and to differentiate between dynamic and static work of the back in an experimental setup, based on a machine learning (ML) approach. A movement protocol was designed to cover the essential degrees of freedom of the back, and a subject wearing a triaxial accelerometer implemented this protocol. Impulses and oscillations from the signals were removed by median filtering, then the filtered dataset was fed into two ML algorithms, namely a multilayer perceptron with back propagation (MLPBNN) and a random forest (RF), with the aim of inferring the most suitable algorithm and architecture for detecting dynamic and static work, as well as for correctly classifying the postures of the back. Then, training and testing subsets were delimitated and used to evaluate the learning and generalization ability of the ML algorithms for the same classification problems. The results indicate that ML has a lot of potential in differentiating between dynamic and static work, depending on the type of algorithm and its architecture, and the data quantity and quality. In particular, MLPBNN can be used to better differentiate between dynamic and static work when tuned properly. In addition, static work and the associated postures were better learned and generalized by the MLPBNN, a fact that could provide the basis for cheap real-world offline applications with the aim of getting time-scaled postural profiling data by accounting for the static postures. Although it wasn’t the case in this study, on bigger datasets, the use of MLPBPNN may come at the expense of high computational costs in the training phase. The study also discusses the factors that may improve the classification performance in the testing phase and sets new directions of research.

Consensus Paper: Ataxic Gait

The aim of this consensus paper is to discuss the roles of the cerebellum in human gait, as well as its assessment and therapy. Cerebellar vermis is critical for postural control. The cerebellum ensures the mapping of sensory information into temporally relevant motor commands. Mental imagery of gait involves intrinsically connected fronto-parietal networks comprising the cerebellum. Muscular activities in cerebellar patients show impaired timing of discharges, affecting the patterning of the synergies subserving locomotion. Ataxia of stance/gait is amongst the first cerebellar deficits in cerebellar disorders such as degenerative ataxias and is a disabling symptom with a high risk of falls. Prolonged discharges and increased muscle coactivation may be related to compensatory mechanisms and enhanced body sway, respectively. Essential tremor is frequently associated with mild gait ataxia. There is growing evidence for an important role of the cerebellar cortex in the pathogenesis of essential tremor. In multiple sclerosis, balance and gait are affected due to cerebellar and spinal cord involvement, as a result of disseminated demyelination and neurodegeneration impairing proprioception. In orthostatic tremor, patients often show mild-to-moderate limb and gait ataxia. The tremor generator is likely located in the posterior fossa. Tandem gait is impaired in the early stages of cerebellar disorders and may be particularly useful in the evaluation of pre-ataxic stages of progressive ataxias. Impaired inter-joint coordination and enhanced variability of gait temporal and kinetic parameters can be grasped by wearable devices such as accelerometers. Kinect is a promising low cost technology to obtain reliable measurements and remote assessments of gait. Deep learning methods are being developed in order to help clinicians in the diagnosis and decision-making process. Locomotor adaptation is impaired in cerebellar patients. Coordinative training aims to improve the coordinative strategy and foot placements across strides, cerebellar patients benefiting from intense rehabilitation therapies. Robotic training is a promising approach to complement conventional rehabilitation and neuromodulation of the cerebellum. Wearable dynamic orthoses represent a potential aid to assist gait. The panel of experts agree that the understanding of the cerebellar contribution to gait control will lead to a better management of cerebellar ataxias in general and will likely contribute to use gait parameters as robust biomarkers of future clinical trials.

Gait Impairment in Traumatic Brain Injury: A Systematic Review

Introduction: Gait impairment occurs across the spectrum of traumatic brain injury (TBI); from mild (mTBI) to moderate (modTBI), to severe (sevTBI). Recent evidence suggests that objective gait assessment may be a surrogate marker for neurological impairment such as TBI. However, the most optimal method of objective gait assessment is still not well understood due to previous reliance on subjective assessment approaches. The purpose of this review was to examine objective assessment of gait impairments across the spectrum of TBI. Methods: PubMed, AMED, OVID and CINAHL databases were searched with a search strategy containing key search terms for TBI and gait. Original research articles reporting gait outcomes in adults with TBI (mTBI, modTBI, sevTBI) were included. Results: 156 citations were identified from the search, of these, 13 studies met the initial criteria and were included into the review. The findings from the reviewed studies suggest that gait is impaired in mTBI, modTBI and sevTBI (in acute and chronic stages), but methodological limitations were evident within all studies. Inertial measurement units were most used to assess gait, with single-task, dual-task and obstacle crossing conditions used. No studies examined gait across the full spectrum of TBI and all studies differed in their gait assessment protocols. Recommendations for future studies are provided. Conclusion: Gait was found to be impaired in TBI within the reviewed studies regardless of severity level (mTBI, modTBI, sevTBI), but methodological limitations of studies (transparency and reproducibility) limit clinical application. Further research is required to establish a standardised gait assessment procedure to fully determine gait impairment across the spectrum of TBI with comprehensive outcomes and consistent protocols.

Accelerometry-Based Metrics to Evaluate the Relative Use of the More Affected Arm during Daily Activities in Adults Living with Cerebral Palsy

Adults living with cerebral palsy (CP) report bimanual and unimanual difficulties that interfere with their participation in activities of daily living (ADL). There is a lack of quantitative methods to assess the impact of these motor dysfunctions on the relative use of each arm. The objective of this study was to evaluate the concurrent and discriminative validity of accelerometry-based metrics when used to assess bimanual and unimanual functions.

METHODS

A group of control subjects and hemiplegic adults living with CP performed six ADL tasks, during which they were wearing an Actigraph GT9X on each wrist and being filmed. Four bimanual and unimanual metrics were calculated from both accelerometry-based and video-based data; these metrics were then compared to one other with an intraclass correlation coefficient (ICC). Some of these metrics were previously validated in other clinical population, while others were novel. The discriminative validity was assessed through comparisons between groups and between tasks.

RESULTS

The concurrent validity was considered as good to excellent (ICC = 0.61-0.97) depending on the experience of the raters. The tasks made it possible to discriminate between groups.

CONCLUSION

The proposed accelerometry-based metrics are a promising tool to evaluate bimanual and unimanual functions in adults living with CP.

Accelerometer-based prediction of ground reaction force in head-out water exercise with different exercise intensity countermovement jump

Background

Water jumping exercise is an alternative method to achieve maintenance of bone health and reduce exercise injuries. Clarifying the ground reaction force (GRF) of moderate and high cardiopulmonary exercise intensities for jumping movements can help quantify the impact force during different exercise intensities. Accelerometers have been explored for measuring skeletal mechanical loading by estimating the GRFs. Predictive regression equations for GRF using ACC on land have already been developed and performed outside laboratory settings, whereas a predictive regression equation for GRF in water exercises is not yet established. The purpose of this study was to determine the best accelerometer wear-position for three exercise intensities and develop and validate the ground reaction force (GRF) prediction equation.

Methods

Twelve healthy women (23.6 ± 1.83 years, 158.2 ± 5.33 cm, 53.1 ± 7.50 kg) were recruited as participants. Triaxial accelerometers were affixed 3 cm above the medial malleolus of the tibia, fifth lumbar vertebra, and seventh cervical vertebra (C7). The countermovement jump (CMJ) cadence started at 80 beats/min and increased by 5 beats per 20 s to reach 50%, 65%, and 80% heart rate reserves, and then participants jumped five more times. One-way repeated analysis of variance was used to determine acceleration differences among wear-positions and exercise intensities. Pearson’s correlation was used to determine the correlation between the acceleration and GRF per body weight on land (GRF_VL_BW). Backward regression analysis was used to generate GRF_VL_BW prediction equations from full models with C7 acceleration (C7 ACC), age, percentage of water deep divided by body height (PWDH), and bodyweight as predictors. Paired t-test was used to determine GRF_VL_BW differences between values from the prediction equation and force plate measurement during validation. Lin’s CCC and Bland–Altman plots were used to determine the agreement between the predicted and force plate-measured GRF_VL_BW.

Results

The raw full profile data for the resultant acceleration showed that the acceleration curve of C7 was similar to that of GRFv. The predicted formula was − 1.712 + 0.658 * C7ACC + 0.016 * PWDH + 0.008 * age + 0.003*weight. Lin’s CCC score was 0.7453, with bias of 0.369%.

Conclusion

The resultant acceleration measured at C7 was identified as the valid estimated GRF_VL_BW during CMJ in water.

Predicting atrial fibrillation episodes with rapid ventricular rates associated with low levels of activity

BACKGROUND

Rapid and irregular ventricular rates (RVR) are an important consequence of atrial fibrillation (AF). Raw accelerometry data in combination with electrocardiogram (ECG) data have the potential to distinguish inappropriate from appropriate tachycardia in AF. This can allow for the development of a just-in-time intervention for clinical treatments of AF events. The objective of this study is to develop a machine learning algorithm that can distinguish episodes of AF with RVR that are associated with low levels of activity.

METHODS

This study involves 45 patients with persistent or paroxysmal AF. The ECG and accelerometer data were recorded continuously for up to 3 weeks. The prediction of AF episodes with RVR and low activity was achieved using a deterministic probabilistic finite-state automata (DPFA)-based approach. Rapid and irregular ventricular rate (RVR) is defined as having heart rates (HR) greater than 110 beats per minute (BPM) and high activity is defined as greater than 0.75 quantile of the activity level. The AF events were annotated using the FDA-cleared BeatLogic algorithm. Various time intervals prior to the events were used to determine the longest prediction intervals for predicting AF with RVR episodes associated with low levels of activity.

RESULTS

Among the 961 annotated AF events, 292 met the criterion for RVR episode. There were 176 and 116 episodes with low and high activity levels respectively. Out of the 961 AF episodes, 770 (80.1%) were used in the training data set and the remaining 191 intervals were held out for testing. The model was able to predict AF with RVR and low activity up to 4.5 min before the events. The mean prediction performance gradually decreased as the time to events increased. The overall Area under the ROC Curve (AUC) for the model lies within the range of 0.67-0.78.

CONCLUSION

The DPFA algorithm can predict AF with RVR associated with low levels of activity up to 4.5 min before the onset of the event. This would enable the development of just-in-time interventions that could reduce the morbidity and mortality associated with AF and other similar arrhythmias.

Applications of Wearable Technology in a Real-Life Setting in People with Knee Osteoarthritis: A Systematic Scoping Review

With the growing number of people affected by osteoarthritis, wearable technology may enable the provision of care outside a traditional clinical setting and thus transform how healthcare is delivered for this patient group. Here, we mapped the available empirical evidence on the utilization of wearable technology in a real-world setting in people with knee osteoarthritis. From an analysis of 68 studies, we found that the use of accelerometers for physical activity assessment is the most prevalent mode of use of wearable technology in this population. We identify low technical complexity and cost, ability to connect with a healthcare professional, and consistency in the analysis of the data as the most critical facilitators for the feasibility of using wearable technology in a real-world setting. To fully realize the clinical potential of wearable technology for people with knee osteoarthritis, this review highlights the need for more research employing wearables for information sharing and treatment, increased inter-study consistency through standardization and improved reporting, and increased representation of vulnerable populations.

Developing and exploring a methodology for multi-modal indoor and outdoor gait assessment

Gait assessment is emerging as a prominent way to understand impaired mobility and underlying neurological deficits. Various technologies have been used to assess gait inside and outside of laboratory settings, but wearables are the preferred option due to their cost-effective and practical use in both. There are robust conceptual gait models developed to ease the interpretation of gait parameters during indoor and outdoor environments. However, these models examine uni-modal gait characteristics (e.g., spatio-temporal parameters) only. Previous studies reported that understanding the underlying reason for impaired gait requires multi-modal gait assessment. Therefore, this study aims to develop a multi-modal approach using a synchronized inertial and electromyography (EMG) signals. Firstly, initial contact (IC), final contact (FC) moments and corresponding time stamps were identified from inertial data, producing temporal outcomes e.g., step time. Secondly, IC/FC time stamps were used to segment EMG data and define onset and offset times of muscle activities within the gait cycle and its subphases. For investigation purposes, we observed notable differences in temporal characteristics as well as muscle onset/offset timings and amplitudes between indoor and outdoor walking of three stroke survivors. Our preliminary analysis suggests a multi-modal approach may be important to augment and improve current inertial conceptual gait models by providing additional quantitative EMG data.

Detecting subtle yet fast skeletal muscle contractions with ultrasoft and durable graphene-based cellular materials

Human bodily movements are primarily controlled by the contractions of skeletal muscles. Unlike joint or skeletal movements that are generally performed in the large displacement range, the contractions of the skeletal muscles that underpin these movements are subtle in intensity yet high in frequency. This subtlety of movement makes it a formidable challenge to develop wearable and durable soft materials to electrically monitor such motions with high fidelity for the purpose of, for example, muscle/neuromuscular disease diagnosis. Here we report that an intrinsically fragile ultralow-density graphene-based cellular monolith sandwiched between silicone rubbers can exhibit a highly effective stress and strain transfer mechanism at its interface with the rubber, with a remarkable improvement in stretchability (>100%). In particular, this hybrid also exhibits a highly sensitive, broadband-frequency electrical response (up to 180 Hz) for a wide range of strains. By correlating the mechanical signal of muscle movements obtained from this hybrid material with electromyography, we demonstrate that the strain sensor based on this hybrid material may provide a new, soft and wearable mechanomyography approach for real-time monitoring of complex neuromuscular–skeletal interactions in a broad range of healthcare and human–machine interface applications. This work also provides a new architecture-enabled functional soft material platform for wearable electronics.

Reconstruction of Pulse Wave and Respiration from Wrist Accelerometer During Sleep

OBJECTIVE

Nocturnal recordings of heart rate and respiratory rate usually require several separate sensors or electrodes attached to different body parts -- a disadvantage for at-home screening tests and for large cohort studies. In this paper, we demonstrate that a state-of-the-art accelerometer placed at subjects' wrists can be used to derive reliable signal reconstructions of heartbeat (pulse wave intervals) and respiration during sleep.

METHODS

Based on 226 full-night recordings, we evaluate the performance of our signal reconstruction methodology with respect to polysomnography. We use a phase synchronization analysis metrics that considers individual heartbeats or breaths.

RESULTS

The quantitative comparison reveals that pulse-wave signal reconstructions are generally better than respiratory signal reconstructions. The best quality is achieved during deep sleep, followed by light sleep N2 and REM sleep. In addition, a suggested internal evaluation of multiple derived reconstructions can be used to identify time periods with highly reliable signals, particularly for pulse waves. Furthermore, we find that pulse-wave reconstructions are hardly affected by apnea and hypopnea events.

CONCLUSION

During sleep, pulse wave and respiration signals can simultaneously be reconstructed from the same accelerometer recording at the wrist without the need for additional sensors. Reliability can be increased by internal evaluation if the reconstructed signals are not needed for the whole sleep duration.

SIGNIFICANCE

The presented methodology can help to determine sleep characteristics and improve diagnostics and treatment of sleep disorders in the subjects' normal sleep environment.

Three-Day Remote Monitoring of Gait Among Young and Older Adults Using Participants' Personal Smartphones

Conventional one-time gait analyses do not evaluate walking across more than a few steps, cannot monitor changes longitudinally, and do not reflect performance in real-life environments. To successfully quantify age-related gait decrement, technology that can continuously monitor gait is vital. This study examined the feasibility and validity for participant smartphones to remotely assess gait. In addition, the authors investigated whether smartphone-derived measures could differentiate between young and older adults (fallers and nonfallers). A total of 63 adults completed clinical and gait assessment in the laboratory and donned their smartphones for 3 days in the real-life environment. A custom-built Android application collected triaxial accelerations with spatiotemporal gait measures computed and compared between groups. Across 11 brands and 10 Android versions, smartphone-derived gait parameters were valid. Furthermore, results indicated age-related differences in walking during the 3-day assessment. However, no disparities were found between older adult groups. Smartphone-based evaluations may improve real-life screening of adults with gait deficits.

Smartphone- and Smartwatch-Based Remote Characterisation of Ambulation in Multiple Sclerosis During the Two-Minute Walk Test

Leveraging consumer technology such as smartphone and smartwatch devices to objectively assess people with multiple sclerosis (PwMS) remotely could capture unique aspects of disease progression. This study explores the feasibility of assessing PwMS and Healthy Control's (HC) physical function by characterising gait-related features, which can be modelled using machine learning (ML) techniques to correctly distinguish subgroups of PwMS from healthy controls. A total of 97 subjects (24 HC subjects, 52 mildly disabled (PwMSmild, EDSS [0-3]) and 21 moderately disabled (PwMSmod, EDSS [3.5-5.5]) contributed data which was recorded from a Two-Minute Walk Test (2MWT) performed out-of-clinic and daily over a 24-week period. Signal-based features relating to movement were extracted from sensors in smartphone and smartwatch devices. A large number of features (n = 156) showed fair-to-strong (R 0.3) correlations with clinical outcomes. LASSO feature selection was applied to select and rank subsets of features used for dichotomous classification between subject groups, which were compared using Logistic Regression (LR), Support Vector Machines (SVM) and Random Forest (RF) models. Classifications of subject types were compared using data obtained from smartphone, smartwatch and the fusion of features from both devices. Models built on smartphone features alone achieved the highest classification performance, indicating that accurate and remote measurement of the ambulatory characteristics of HC and PwMS can be achieved with only one device. It was observed however that smartphone-based performance was affected by inconsistent placement location (running belt versus pocket). Results show that PwMSmod could be distinguished from HC subjects (Acc. 82.2 ± 2.9%, Sen. 80.1 ± 3.9%, Spec. 87.2 ± 4.2%, F ₁ 84.3 ± 3.8), and PwMSmild (Acc. 82.3 ± 1.9%, Sen. 71.6 ± 4.2%, Spec. 87.0 ± 3.2%, F 1 75.1 ± 2.2) using an SVM classifier with a Radial Basis Function (RBF). PwMSmild were shown to exhibit HC-like behaviour and were thus less distinguishable from HC (Acc. 66.4 ± 4.5%, Sen. 67.5 ± 5.7%, Spec. 60.3 ± 6.7%, F ₁ 58.6 ± 5.8). Finally, it was observed that subjects in this study demonstrated low intra- and high inter-subject variability which was representative of subject-specific gait characteristics.

Acceleration Gait Measures as Proxies for Motor Skill of Walking: A Narrative Review

In adults 65 years or older, falls or other neuromotor dysfunctions are often framed as walking-related declines in motor skill; the frequent occurrence of such decline in walking-related motor skill motivates the need for an improved understanding of the motor skill of walking. Simple gait measurements, such as speed, do not provide adequate information about the quality of the body motion's translation during walking. Gait measures from accelerometers can enrich measurements of walking and motor performance. This review article will categorize the aspects of the motor skill of walking and review how trunk-acceleration gait measures during walking can be mapped to motor skill aspects, satisfying a clinical need to understand how well accelerometer measures assess gait. We will clarify how to leverage more complicated acceleration measures to make accurate motor skill decline predictions, thus furthering fall research in older adults.

Application and Reliability of Accelerometer-Based Arm Use Intensities in the Free-Living Environment for Manual Wheelchair Users and Able-Bodied Individuals

Arm use in manual wheelchair (MWC) users is characterized by a combination of overuse and a sedentary lifestyle. This study aimed to describe the percentage of daily time MWC users and able-bodied individuals spend in each arm use intensity level utilizing accelerometers. Arm use intensity levels of the upper arms were defined as stationary, low, mid, and high from the signal magnitude area (SMA) of the segment accelerations based on in-lab MWC activities performed by eight MWC users. Accelerometry data were collected in the free-living environments from forty MWC users and 40 sex- and age-matched able-bodied individuals. The SMA intensity levels were applied to the free-living data and the percentage of time spent in each level was calculated. The SMA intensity levels were defined as, stationary: ≤0.67 g, low: 0.671-3.27 g, mid: 3.27-5.87 g, and high: >5.871 g. The dominant arm of both MWC users and able-bodied individuals was stationary for most of the day and less than one percent of the day was spent in high intensity arm activities. Increased MWC user age correlated with increased stationary arm time (R = 0.368, p = 0.019). Five and eight days of data are needed from MWC users and able-bodied individuals, respectively, to achieve reliable representation of their daily arm use intensities.

Positive affect state is a good predictor of movement and stress: combining data from ESM/EMA, mobile HRV measurements and trait questionnaires

Personality describes the average behaviour and responses of individuals across situations; but personality traits are often poor predictors of behaviour in specific situations. This is known as the "personality paradox". We evaluated the interrelations between various trait and state variables in participants' everyday lives. As state measures, we used 1) experience sampling methodology (ESM/EMA) to measure perceived affect, stress, and presence of social company; and 2) heart rate variability and 3) real-time movement (accelerometer data) to indicate physiological stress and physical movement. These data were linked with self-report measures of personality and personality-like traits. Trait variables predicted affect states and multiple associations were found: traits neuroticism and rumination decreased positive affect state and increased negative affect state. Positive affect state, in turn, was the strongest predictor of observed movement. Positive affect was also associated with heart rate and heart rate variability (HRV). Negative affect, in turn, was not associated with neither movement, HR or HRV. The study provides evidence on the influence of personality-like traits and social context to affect states, and, in turn, their influence to movement and stress variables.

Efficient embedded sleep wake classification for open-source actigraphy

This study presents a thorough analysis of sleep/wake detection algorithms for efficient on-device sleep tracking using wearable accelerometric devices. It develops a novel end-to-end algorithm using convolutional neural network applied to raw accelerometric signals recorded by an open-source wrist-worn actigraph. The aim of the study is to develop an automatic classifier that: (1) is highly generalizable to heterogenous subjects, (2) would not require manual features' extraction, (3) is computationally lightweight, embeddable on a sleep tracking device, and (4) is suitable for a wide assortment of actigraphs. Hereby, authors analyze sleep parameters, such as total sleep time, waking after sleep onset and sleep efficiency, by comparing the outcomes of the proposed algorithm to the gold standard polysomnographic concurrent recordings. The relatively substantial agreement (Cohen's kappa coefficient, median, equal to 0.78 ± 0.07) and the low-computational cost (2727 floating-point operations) make this solution suitable for an on-board sleep-detection approach.

Forward and Inverse Dynamics of a Six-Axis Accelerometer Based on a Parallel Mechanism

The solution of the dynamic equations of the six-axis accelerometer is a prerequisite for sensor calibration, structural optimization, and practical application. However, the forward dynamic equations (FDEs) and inverse dynamic equations (IDEs) of this type of system have not been completely solved due to the strongly nonlinear coupling relationship between the inputs and outputs. This article presents a comprehensive study of the FDEs and IDEs of the six-axis accelerometer based on a parallel mechanism. Firstly, two sets of dynamic equations of the sensor are constructed based on the Newton–Euler method in the configuration space. Secondly, based on the analytical solution of the sensor branch chain length, the coordination equation between the output signals of the branch chain is constructed. The FDEs of the sensor are established by combining the coordination equations and two sets of dynamic equations. Furthermore, by introducing generalized momentum and Hamiltonian function and using Legendre transformation, the vibration differential equations (VDEs) of the sensor are derived. The VDEs and Newton–Euler equations constitute the IDEs of the system. Finally, the explicit recursive algorithm for solving the quaternion in the equation is given in the phase space. Then the IDEs are solved by substituting the quaternion into the dynamic equations in the configuration space. The predicted numerical results of the established FDEs and IDEs are verified by comparing with virtual and actual experimental data. The actual experiment shows that the relative errors of the FDEs and the IDEs constructed in this article are 2.21% and 7.65%, respectively. This research provides a new strategy for further improving the practicability of the six-axis accelerometer.

Gait analysis in neurological populations: Progression in the use of wearables

Gait assessment is an essential tool for clinical applications not only to diagnose different neurological conditions but also to monitor disease progression as it contributes to the understanding of underlying deficits. There are established methods and models for data collection and interpretation of gait assessment within different pathologies. This narrative review aims to depict the evolution of gait assessment from observation and rating scales to wearable sensors and laboratory technologies and provide limitations and possible future directions in the field of gait assessment. In this context, we first present an extensive review of current clinical outcomes and gait models. Then, we demonstrate commercially available wearable technologies with their technical capabilities along with their use in gait assessment studies for various neurological conditions. In the next sections, a descriptive knowledge for existing inertial and EMG based algorithms and a sign based guide that shows the outcomes of previous neurological gait assessment studies are presented. Finally, we state a discussion for the use of wearables in gait assessment and speculate the possible research directions by revealing the limitations and knowledge gaps in the literature.

Validation of Accelerometry Data to Identify Movement Patterns During Agility Testing

Purpose: The purpose of this study was to develop an algorithm for the detection and timing of foot contact/off timing for each lateral repetition and assess the validity/reliability of the calculated timings.

Methods: Participants performed a modified Edgren Side Step Test in which they moved laterally along a 4-m path as quickly as possible while wearing an accelerometer on each ankle. Time of completion of each attempt was recorded using a stopwatch and digital video was obtained. Accelerometer-based (ACC) events were determined for the start of the test (START), foot contact at the end-line (FC) and the lifting of the foot when transitioning to the other direction (FO). Based on these ACC events the Overall, Split (ST) and Lag (LT) times were determined and compared to either the stopwatch or video-based timings (p < 0.05). The ACC event criterion was then applied by independent reviewers to assess inter/intra-rater reliability of identifying the events.

Results: There was no significant difference in ACC (12.37 ± 2.19 s) based Overall Time compared to the Stopwatch (12.42 ± 2.25 s, p = 0.34). Bland-Altman plots for ST and LT revealed very good agreement between the ACC time to the Video (ST: Bias = 0.11 s, LOA −0.57 to 0.79; LT: Bias = −0.11 s, LOA −0.43 to 0.22). Intra and inter-rater reliability was moderate to excellent for all reviewer identified events.

Conclusions: This study demonstrates methodology to identify ACC based timings during an agility test. The inclusion of an accelerometer supplements standard timing options with the added benefit of assessing sided split and lag times.

Smart Helmet 5.0 for Industrial Internet of Things Using Artificial Intelligence

Information and communication technologies (ICTs) have contributed to advances in Occupational Health and Safety, improving the security of workers. The use of Personal Protective Equipment (PPE) based on ICTs reduces the risk of accidents in the workplace, thanks to the capacity of the equipment to make decisions on the basis of environmental factors. Paradigms such as the Industrial Internet of Things (IIoT) and Artificial Intelligence (AI) make it possible to generate PPE models feasibly and create devices with more advanced characteristics such as monitoring, sensing the environment and risk detection between others. The working environment is monitored continuously by these models and they notify the employees and their supervisors of any anomalies and threats. This paper presents a smart helmet prototype that monitors the conditions in the workers’ environment and performs a near real-time evaluation of risks. The data collected by sensors is sent to an AI-driven platform for analysis. The training dataset consisted of 11,755 samples and 12 different scenarios. As part of this research, a comparative study of the state-of-the-art models of supervised learning is carried out. Moreover, the use of a Deep Convolutional Neural Network (ConvNet/CNN) is proposed for the detection of possible occupational risks. The data are processed to make them suitable for the CNN and the results are compared against a Static Neural Network (NN), Naive Bayes Classifier (NB) and Support Vector Machine (SVM), where the CNN had an accuracy of 92.05% in cross-validation.

Improving Energy Expenditure Estimation through Activity Classification and Walking Speed Estimation Using a Smartwatch

Energy expenditure (EE) estimation is an important factor in tracking personal activity and preventing chronic diseases, such as obesity and diabetes. The challenge is to provide accurate EE estimations in free-living environment through portable and unobtrusive devices. In this paper, we present an experimental study to estimate energy expenditure during sitting, standing and treadmill walking using a smartwatch. We introduce a novel methodology, which aims to improve the EE estimation by first separating sedentary (sitting and standing) and non-sedentary (walking) activities, followed by estimating the walking speeds and then calculating the energy expenditure using advanced machine learning based regression models. Ten young adults participated in the experimental trials. Our results showed that combining activity type and walking speed information with the acceleration counts substantially improved the accuracy of regression models for estimating EE. On average, the activity-based models provided 7% better EE estimation than the traditional acceleration-based models.

Accelerometry assessed physical activity of older adults hospitalized with acute medical illness - an observational study

Background

In a hospital setting and among older patients, inactivity and bedrest are associated with a wide range of negative outcomes such as functional decline, increased risk of falls, longer hospitalization and institutionalization. Our aim was to assess the distribution, determinants and predictors of physical activity (PA) levels using wrist-worn accelerometers in older patients hospitalized with acute medical illness.

Methods

Observational study conducted from February to November 2018 at an acute internal medicine unit in the University hospital of Lausanne, Switzerland. We enrolled 177 patients aged ≥65 years, able to walk prior to admission. PA during acute hospital stay was continuously recorded via a 3D wrist accelerometer. Clinical data was collected from medical records or by interview. Autonomy level prior to inclusion was assessed using Barthel Index score. PA levels were defined as < 30 mg for inactivity, 30–99 mg for light and ≥ 100 for moderate PA. Physically active patients were defined as 1) being in the highest quartile of time spent in light and moderate PA or 2) spending ≥20 min/day in moderate PA.

Results

Median [interquartile range - IQR] age was 83 [74–87] years and 60% of participants were male. The median [IQR] time spent inactive and in light PA was 613 [518–663] and 63 [30–97] minutes/day, respectively. PA peaked between 8 and 10 am, at 12 am and at 6 pm. Less than 10% of patients were considered physically active according to definition 2. For both definitions, active patients had a lower prevalence of walking aids and a lower dependency level according to Barthel Index score. For definition 1, use of medical equipment was associated with a 70% reduction in the likelihood of being active: odds ratio (OR) 0.30 [0.10–0.92] p = 0.034; for definition 2, use of walking aids was associated with a 75% reduction in the likelihood of being active: OR = 0.24 [0.06–0.89], p = 0.032.

Conclusion

Older hospitalized patients are physically active only 10% of daily time and concentrate their PA around eating periods. Whether a Barthel Index below 95 prior to admission may be used to identify patients at risk of inactivity during hospital stay remains to be proven.

Postural symmetry evaluation using phase approximations of the follow-up CoP trajectories

This paper presents a method of postural symmetry evaluation implementing the so-called follow-up posturography. The method boils down to the assessment of similarity of the phase approximation of the counter-clockwise follow-up CoP (Center of Pressure) trajectory and the mirror image against the y-axis of the phase approximation corresponding to the clockwise follow-up CoP trajectory. The usability of the presented approach was tested on the data collected in the group of 30 patients rehabilitated after total hip arthroplasty. The observed difference between the values of the proposed postural symmetry coefficient obtained at the end and at the beginning of the rehabilitation program was statistically significant (p < 0.001). These values, however, were not significantly correlated with the values of postural symmetry coefficients computed in static posturography. Lack of significant correlations between the coefficients supports the reasoning that the new postural symmetry evaluation method quantifies symmetry of posture in terms of dynamic mechanisms, which are not manifested in the case of static posturography. As a major advantage of the herein discussed approach one can distinguish its potential to evaluate postural symmetry in dynamic conditions using relatively inexpensive single-plate posturographic platform.

Calibration and validation of accelerometry using cut-points to assess physical activity in paediatric clinical groups: A systematic review

Regular physical activity is associated with physiological and psychosocial benefits in both healthy and clinical populations. However, little is known about tailoring the analysis of physical activity using accelerometers to the specific characteristics of chronic conditions. Whilst accelerometry is broadly used to assess physical activity, recommendations on calibration in paediatric clinical groups are warranted. The aim of this systematic review was to provide a critical overview of protocols used to calibrate accelerometry in children and adolescents with clinical conditions, as well as to develop recommendations for calibration and validation of accelerometry in such populations. The search was performed between March to July 2017 using text words and subject headings in six databases. Studies had to develop moderate-to-vigorous intensity physical activity (MVPA) cut-points for paediatric clinical populations to be included. Risk of bias was assessed using a specific checklist. A total of 540,630 titles were identified, with 323 full-text articles assessed. Five studies involving 347 participants aged 9 to 15 years were included. Twenty-four MVPA cut-points were reported across seven clinical conditions, 16 of which were developed for different models of ActiGraph, seven for Actical and one for Tritrac-R3D. Statistical approaches included mixed regression, machine learning and receiver operating characteristic analyses. Disease-specific MVPA cut-points ranged from 152 to 735 counts·15 s-1, with lower cut-points found for inherited muscle disease and higher cut-points associated with intellectual disabilities. The lower MVPA cut-points for diseases characterised by both ambulatory and metabolic impairments likely reflect the higher energetic demands associated with those conditions.

Fit-for-Purpose Biometric Monitoring Technologies: Leveraging the Laboratory Biomarker Experience

Biometric monitoring technologies (BioMeTs) are becoming increasingly common to aid data collection in clinical trials and practice. The state of BioMeTs, and associated digitally measured biomarkers, is highly reminiscent of the field of laboratory biomarkers 2 decades ago. In this review, we have summarized and leveraged historical perspectives, and lessons learned from laboratory biomarkers as they apply to BioMeTs. Both categories share common features, including goals and roles in biomedical research, definitions, and many elements of the biomarker qualification framework. They can also be classified based on the underlying technology, each with distinct features and performance characteristics, which require bench and human experimentation testing phases. In contrast to laboratory biomarkers, digitally measured biomarkers require prospective data collection for purposes of analytical validation in human subjects, lack well‐established and widely accepted performance characteristics, require human factor testing, and, for many applications, access to raw (sample‐level) data. Novel methods to handle large volumes of data, as well as security and data rights requirements add to the complexity of this emerging field. Our review highlights the need for a common framework with appropriate vocabulary and standardized approaches to evaluate digitally measured biomarkers, including defining performance characteristics and acceptance criteria. Additionally, the need for human factor testing drives early patient engagement during technology development. Finally, use of BioMeTs requires a relatively high degree of technology literacy among both study participants and healthcare professionals. Transparency of data generation and the need for novel analytical and statistical tools creates opportunities for precompetitive collaborations.

Piezoelectric Sensor with a Helical Structure on the Thread Core

In this paper, we introduce a piezoelectric sensor curled on a thread core in a helical structure. In particular, a polyvinylidene fluoride film was curled and fixed on a thread core. A series of experiments were designed to deliver flexural loading to the piezoelectric sensor, to study its sensing characteristics. The experimental results show that the sensing output of the sensor is in phase with the applied flexural loading. In addition, the output voltage of the textile-based piezoelectric sensor was measured according to various flexural loadings. The flexural bending angle applied to the piezoelectric sensor is expected to be a power function of the voltage output. In addition, we demonstrate a smart textile by weaving the piezoelectric sensor.

Accelerometer-based prediction of skeletal mechanical loading during walking in normal weight to severely obese subjects

There is no objective way to monitor mechanical loading characteristics during exercise for bone health improvement. We developed accelerometry-based equations to predict ground reaction force (GRF) and loading rate (LR) in normal weight to severely obese subjects. Equations developed had a high and moderate accuracy for GRF and LR prediction, respectively, thereby representing an accessible way to determine mechanical loading characteristics in clinical settings.

INTRODUCTION

There is no way to objectively prescribe and monitor exercise for bone health improvement in obese patients based on mechanical loading characteristics. We aimed to develop accelerometry-based equations to predict peak ground reaction forces (pGRFs) and peak loading rate (pLR) on normal weight to severely obese subjects.

METHODS

Sixty-four subjects (45 females; 84.6 ± 21.7 kg) walked at different speeds (2-6 km·h^-1) on a force plate-equipped treadmill while wearing accelerometers at lower back and hip. Regression equations were developed to predict pGRF and pLR from accelerometry data. Leave-one-out cross-validation was used to calculate prediction accuracy and Bland-Altman plots. Actual and predicted values at different speeds were compared by repeated measures ANOVA.

RESULTS

Body mass and peak acceleration were included for pGRF prediction and body mass and peak acceleration transient rate for pLR prediction. All pGRF equation coefficients of determination were above 0.89, a good agreement between actual and predicted pGRFs, with a mean absolute percent error (MAPE) below 6.7%. No significant differences were observed between actual and predicted pGRFs at each walking speed. Accuracy indices from our equations were better than previously developed equations for normal weight subjects, namely a MAPE approximately 3 times smaller. All pLR prediction equations presented a lower accuracy compared to those developed to predict pGRF.

CONCLUSION

Walking pGRF and pLR in normal weight to severely obese subjects can be predicted with moderate to high accuracy by accelerometry-based equations, representing an easy and accessible way to determine mechanical loading characteristics in clinical settings.

Determining validity of the PALite and ODFS PACE activity logger for measuring step count in healthy adults

BACKGROUND

Determining adherence with orthoses is important for clinicians prescribing devices. Measuring orthotic use often relies on patient recall which has poor agreement with objective measures. Measuring step count whilst wearing an orthosis could help objectively quantifying adherence. The Odstock Drop Foot Stimulator (ODFS) Pace, used in foot drop, has an integral activity logger which provides data on step count. The PALite, an accelerometer, measures step count and can be fixed to an ankle foot orthoses (AFO). Both have the potential to provide objective measures of adherence; however, their validity for this purpose has not been determined.

RESEARCH QUESTION

To determine the validity of the PALite and ODFS Pace activity logger in measuring total step count, by exploring their level of agreement.

METHODS

A convenience sample of sixteen healthy volunteers, aged 18-65, were recruited from Glasgow Caledonian University (GCU). Participants walked continuously for 5 min on a treadmill at three walking speeds; normal (1.3ms^-1), slow (0.4 ms^-1) and fast (1.7-2.0 ms^-1), wearing both the PALite and ODFS Pace. All walks were video recorded, viewed by 2 raters, and observed step count was determined by a click counter. Step count from both devices was validated against observed step count using video recording. The level of agreement between the three methods was established.

RESULTS

There was no significant difference between the 3 methods of measuring step count at any walking speed (normal, p = 0.913; slow, p = 0.938; fast, p = 0.566). Good levels of agreement for both devices with observed step count at all 3 walking speeds, with mean percentage differences being between -1.2 and 2.1 % (maximum upper and lower levels of agreement = 19.5 and -18.8 %) was detected.

SIGNIFICANCE

Clinicians could consider both devices to objectively measure step count with people who are prescribed foot drop orthoses, thus quantifying orthotic use.