Estimation of Gait Parameters for Adults with Surface Electromyogram Based on Machine Learning Models.

Gait analysis has been studied over the last few decades as the best way to objectively assess the technical outcome of a procedure designed to improve gait. The treating physician can understand the type of gait problem, gain insight into the etiology, and find the best treatment with gait analysis. The gait parameters are the kinematics, including the temporal and spatial parameters, and lack the activity information of skeletal muscles. Thus, the gait analysis measures not only the three-dimensional temporal and spatial graphs of kinematics but also the surface electromyograms (sEMGs) of the lower limbs. Now, the shoe-worn GaitUp Physilog® wearable inertial sensors can easily measure the gait parameters when subjects are walking on the general ground. However, it cannot measure muscle activity. The aim of this study is to measure the gait parameters using the sEMGs of the lower limbs. A self-made wireless device was used to measure the sEMGs from the vastus lateralis and gastrocnemius muscles of the left and right feet. Twenty young female subjects with a skeletal muscle index (SMI) below 5.7 kg/m2 were recruited for this study and examined by the InBody 270 instrument. Four parameters of sEMG were used to estimate 23 gait parameters. They were measured using the GaitUp Physilog® wearable inertial sensors with three machine learning models, including random forest (RF), decision tree (DT), and XGBoost. The results show that 14 gait parameters could be well-estimated, and their correlation coefficients are above 0.800. This study signifies a step towards a more comprehensive analysis of gait with only sEMGs.

American Sign Language Recognition and Translation Using Perception Neuron Wearable Inertial Motion Capture System.

Sign language is designed as a natural communication method to convey messages among the deaf community. In the study of sign language recognition through wearable sensors, the data sources are limited, and the data acquisition process is complex. This research aims to collect an American sign language dataset with a wearable inertial motion capture system and realize the recognition and end-to-end translation of sign language sentences with deep learning models. In this work, a dataset consisting of 300 commonly used sentences is gathered from 3 volunteers. In the design of the recognition network, the model mainly consists of three layers: convolutional neural network, bi-directional long short-term memory, and connectionist temporal classification. The model achieves accuracy rates of 99.07% in word-level evaluation and 97.34% in sentence-level evaluation. In the design of the translation network, the encoder-decoder structured model is mainly based on long short-term memory with global attention. The word error rate of end-to-end translation is 16.63%. The proposed method has the potential to recognize more sign language sentences with reliable inertial data from the device.

Deep Wavelet Convolutional Neural Networks for Multimodal Human Activity Recognition Using Wearable Inertial Sensors.

Recent advances in wearable systems have made inertial sensors, such as accelerometers and gyroscopes, compact, lightweight, multimodal, low-cost, and highly accurate. Wearable inertial sensor-based multimodal human activity recognition (HAR) methods utilize the rich sensing data from embedded multimodal sensors to infer human activities. However, existing HAR approaches either rely on domain knowledge or fail to address the time-frequency dependencies of multimodal sensor signals. In this paper, we propose a novel method called deep wavelet convolutional neural networks (DWCNN) designed to learn features from the time-frequency domain and improve accuracy for multimodal HAR. DWCNN introduces a framework that combines continuous wavelet transforms (CWT) with enhanced deep convolutional neural networks (DCNN) to capture the dependencies of sensing signals in the time-frequency domain, thereby enhancing the feature representation ability for multiple wearable inertial sensor-based HAR tasks. Within the CWT, we further propose an algorithm to estimate the wavelet scale parameter. This helps enhance the performance of CWT when computing the time-frequency representation of the input signals. The output of the CWT then serves as input for the proposed DCNN, which consists of residual blocks for extracting features from different modalities and attention blocks for fusing these features of multimodal signals. We conducted extensive experiments on five benchmark HAR datasets: WISDM, UCI-HAR, Heterogeneous, PAMAP2, and UniMiB SHAR. The experimental results demonstrate the superior performance of the proposed model over existing competitors.

Mobility Rehab visual feedback system for gait rehabilitation in older adults.

BACKGROUND: Gait and balance impairments are among the main causes of falls in older adults. The feasibility and effectiveness of adding sensor-based feedback to physical therapy (PT) in an outpatient PT setting is unknown. We evaluated the feasibility and effectiveness of PT intervention combined with a therapist-assisted visual feedback system, called Mobility Rehab, (PT + MR) in older adults. METHODS: Twenty-eight older adults with and without neurological diseases were assigned either PT + MR (n = 22) or PT alone (n = 6). Both groups performed 8 sessions (individualized) of 45 min long (30 min for gait training and 15 min for endurance, strength, and balance exercises) in an outpatient clinic. Mobility Rehab uses unobtrusive, inertial sensors on both wrists and feet, and at the sternum level with real-time algorithms to provide real-time feedback on five gait metrics (step duration, stride length, elevation at mid-swing, arm swing range-of-motion [ROM], and trunk coronal ROM), which are displayed on a tablet. The primary outcome was the Activities-specific Balance Confidence scale (ABC). The secondary outcome was gait speed measured with wearable inertial sensors during 2 min of walking. RESULTS: There were no between-group differences at baseline for any variable (P > 0.05). Neither PT + MR nor PT alone showed significant changes on the ABC scores. PT + MR, but not PT alone, showed significant improvements in gait speed and arm swing ROM. The system was evaluated as 'easy to use' by the PT. CONCLUSIONS: Our preliminary results show that PT + MR improves gait speed in older adults with and without neurological diseases in an outpatient clinic. CLINICAL TRIAL REGISTRATION: www. CLINICALTRIALS: gov , identifier: NCT03869879.

Innovative multidimensional gait evaluation using IMU in multiple sclerosis: introducing the semiogram.

Background: Quantifying gait using inertial measurement units has gained increasing interest in recent years. Highly degraded gaits, especially in neurological impaired patients, challenge gait detection algorithms and require specific segmentation and analysis tools. Thus, the outcomes of these devices must be rigorously tested for both robustness and relevancy in order to recommend their routine use. In this study, we propose a multidimensional score to quantify and visualize gait, which can be used in neurological routine follow-up. We assessed the reliability and clinical coherence of this method in a group of severely disabled patients with progressive multiple sclerosis (pMS), who display highly degraded gait patterns, as well as in an age-matched healthy subjects (HS) group. Methods: Twenty-two participants with pMS and nineteen HS were included in this 18-month longitudinal follow-up study. During the follow-up period, all participants completed a 10-meter walk test with a U-turn and back, twice at M0, M6, M12, and M18. Average speed and seven clinical criteria (sturdiness, springiness, steadiness, stability, smoothness, synchronization, and symmetry) were evaluated using 17 gait parameters selected from the literature. The variation of these parameters from HS values was combined to generate a multidimensional visual tool, referred to as a semiogram. Results: For both cohorts, all criteria showed moderate to very high test-retest reliability for intra-session measurements. Inter-session quantification was also moderate to highly reliable for all criteria except smoothness, which was not reliable for HS participants. All partial scores, except for the stability score, differed between the two populations. All partial scores were correlated with an objective but not subjective quantification of gait severity in the pMS population. A deficit in the pyramidal tract was associated with altered scores in all criteria, whereas deficits in cerebellar, sensitive, bulbar, and cognitive deficits were associated with decreased scores in only a subset of gait criteria. Conclusions: The proposed multidimensional gait quantification represents an innovative approach to monitoring gait disorders. It provides a reliable and informative biomarker for assessing the severity of gait impairments in individuals with pMS. Additionally, it holds the potential for discriminating between various underlying causes of gait alterations in pMS.

Balance telerehabilitation and wearable technology for people with Parkinson's disease (TelePD trial).

BACKGROUND: Balance impairments, that lead to falls, are one of the main symptoms of Parkinson's disease (PD). Telerehabilitation is becoming more common for people with PD; however, balance is particularly challenging to assess and treat virtually. The feasibility and efficacy of virtual assessment and virtual treatment of balance in people with PD are unknown. The present study protocol has three aims: I) to determine if a virtual balance and gait assessment (instrumented L-shape mobility test) with wearable sensors can predict a gold-standard, in-person clinical assessment of balance, the Mini Balance Evaluation Systems Test (Mini-BESTest); II) to explore the effects of 12 sessions of balance telerehabilitation and unsupervised home exercises on balance, gait, executive function, and clinical scales; and III) to explore if improvements after balance telerehabilitation transfer to daily-life mobility, as measured by instrumented socks with inertial sensors worn for 7 days. METHODS: The TelePD Trial is a prospective, single-center, parallel-group, single-blind, pilot, randomized, controlled trial. This trial will enroll 80 eligible people with PD. Participants will be randomized at a 1:1 ratio into receiving home-based balance exercises in either: 1) balance telerehabilitation (experimental group, n = 40) or 2) unsupervised exercises (control group, n = 40). Both groups will perform 12 sessions of exercise at home that are 60 min long. The primary outcome will be Mini-BESTest. The secondary outcomes will be upper and lower body gait metrics from a prescribed task (instrumented L-shape mobility test); daily-life mobility measures over 7 days with wearable sensors in socks, instrumented executive function tests, and clinical scales. Baseline testing and 7 days of daily-life mobility measurement will occur before and after the intervention period. CONCLUSION: The TelePD Trial will be the first to explore the usefulness of using wearable sensor-based measures of balance and gait remotely to assess balance, the feasibility and efficacy of balance telerehabilitation in people with PD, and the translation of balance improvements after telerehabilitation to daily-life mobility. These results will help to develop a more effective home-based balance telerehabilitation and virtual assessment that can be used remotely in people with balance impairments. TRIAL REGISTRATION: This trial was prospectively registered on ClinicalTrials.gov (NCT05680597).

A Novel Gait Phase Recognition Method Based on DPF-LSTM-CNN Using Wearable Inertial Sensors.

Gait phase recognition is of great importance in the development of rehabilitation devices. The advantages of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) are combined (LSTM-CNN) in this paper, then a gait phase recognition method based on LSTM-CNN neural network model is proposed. In the LSTM-CNN model, the LSTM layer is used to process temporal sequences and the CNN layer is used to extract features A wireless sensor system including six inertial measurement units (IMU) fixed on the six positions of the lower limbs was developed. The difference in the gait recognition performance of the LSTM-CNN model was estimated using different groups of input data collected by seven different IMU grouping methods. Four phases in a complete gait were considered in this paper including the supporting phase with the right hill strike (SU-RHS), left leg swimming phase (SW-L), the supporting phase with the left hill strike (SU-LHS), and right leg swimming phase (SW-R). The results show that the best performance of the model in gait recognition appeared based on the group of data from all the six IMUs, with the recognition precision and macro-F1 unto 95.03% and 95.29%, respectively. At the same time, the best phase recognition accuracy for SU-RHS and SW-R appeared and up to 96.49% and 95.64%, respectively. The results also showed the best phase recognition accuracy (97.22%) for SW-L was acquired based on the group of data from four IMUs located at the left and right thighs and shanks. Comparably, the best phase recognition accuracy (97.86%) for SU-LHS was acquired based on the group of data from four IMUs located at left and right shanks and feet. Ulteriorly, a novel gait recognition method based on Data Pre-Filtering Long Short-Term Memory and Convolutional Neural Network (DPF-LSTM-CNN) model was proposed and its performance for gait phase recognition was evaluated. The experiment results showed that the recognition accuracy reached 97.21%, which was the highest compared to Deep convolutional neural networks (DCNN) and CNN-LSTM.

Human gait-labeling uncertainty and a hybrid model for gait segmentation.

Motion capture systems are widely accepted as ground-truth for gait analysis and are used for the validation of other gait analysis systems. To date, their reliability and limitations in manual labeling of gait events have not been studied. Objectives: Evaluate manual labeling uncertainty and introduce a hybrid stride detection and gait-event estimation model for autonomous, long-term, and remote monitoring. Methods: Estimate inter-labeler inconsistencies by computing the limits-of-agreement. Develop a hybrid model based on dynamic time warping and convolutional neural network to identify valid strides and eliminate non-stride data in inertial (walking) data collected by a wearable device. Finally, detect gait events within a valid stride region. Results: The limits of inter-labeler agreement for key gait events heel off, toe off, heel strike, and flat foot are 72, 16, 24, and 80 ms, respectively; The hybrid model's classification accuracy for stride and non-stride are 95.16 and 84.48%, respectively; The mean absolute error for detected heel off, toe off, heel strike, and flat foot are 24, 5, 9, and 13 ms, respectively, when compared to the average human labels. Conclusions: The results show the inherent labeling uncertainty and the limits of human gait labeling of motion capture data; The proposed hybrid-model's performance is comparable to that of human labelers, and it is a valid model to reliably detect strides and estimate the gait events in human gait data. Significance: This work establishes the foundation for fully automated human gait analysis systems with performances comparable to human-labelers.

Accuracy and comparison of sensor-based gait speed estimations under standardized and daily life conditions in children undergoing rehabilitation.

BACKGROUND: Gait speed is a widely used outcome measure to assess the walking abilities of children undergoing rehabilitation. It is routinely determined during a walking test under standardized conditions, but it remains unclear whether these outcomes reflect the children's performance in daily life. An ankle-worn inertial sensor provides a usable opportunity to measure gait speed in the children's habitual environment. However, sensor-based gait speed estimations need to be accurate to allow for comparison of the children's gait speed between a test situation and daily life. Hence, the first aim of this study was to determine the measurement error of a novel algorithm that estimates gait speed based on data of a single ankle-worn inertial sensor in children undergoing rehabilitation. The second aim of this study was to compare the children's gait speed between standardized and daily life conditions. METHODS: Twenty-four children with walking impairments completed four walking tests at different speeds (standardized condition) and were monitored for one hour during leisure or school time (daily life condition). We determined accuracy by comparing sensor-based gait speed estimations with a reference method in both conditions. Eventually, we compared individual gait speeds between the two conditions. RESULTS: The measurement error was 0.01 ± 0.07 m/s under the standardized and 0.04 ± 0.06 m/s under the daily life condition. Besides, the majority of children did not use the same speed during the test situation as in daily life. CONCLUSION: This study demonstrates an accurate method to measure children's gait speed during standardized walking tests and in the children's habitual environment after rehabilitation. It only requires a single ankle sensor, which potentially increases wearing time and data quality of measurements in daily life. We recommend placing the sensor on the less affected side, unless the child wears one orthosis. In this latter case, the sensor should be placed on the side with the orthosis. Moreover, this study showed that most children did not use the same speed in the two conditions, which encourages the use of wearable inertial sensors to assess the children's walking performance in their habitual environment following rehabilitation.

Sensor-based outcomes to monitor everyday life motor activities of children and adolescents with neuromotor impairments: A survey with health professionals.

In combination with appropriate data processing algorithms, wearable inertial sensors enable the measurement of motor activities in children's and adolescents' habitual environments after rehabilitation. However, existing algorithms were predominantly designed for adult patients, and their outcomes might not be relevant for a pediatric population. In this study, we identified the needs of pediatric rehabilitation to create the basis for developing new algorithms that derive clinically relevant outcomes for children and adolescents with neuromotor impairments. We conducted an international survey with health professionals of pediatric neurorehabilitation centers, provided them a list of 34 outcome measures currently used in the literature, and asked them to rate the clinical relevance of these measures for a pediatric population. The survey was completed by 62 therapists, 16 doctors, and 9 nurses of 16 different pediatric neurorehabilitation centers from Switzerland, Germany, and Austria. They had an average work experience of 13 ± 10 years. The most relevant outcome measures were the duration of lying, sitting, and standing positions; the amount of active self-propulsion during wheeling periods; the hand use laterality; and the duration, distance, and speed of walking periods. The health profession, work experience, and workplace had a minimal impact on the priorities of health professionals. Eventually, we complemented the survey findings with the family priorities of a previous study to provide developers with the clinically most relevant outcomes to monitor everyday life motor activities of children and adolescents with neuromotor impairments.

Concurrent validation of the Noraxon MyoMotion wearable inertial sensors in change-of-direction and jump-landing tasks.

Wearable inertial sensors (WIS) facilitate the preservation of the athlete-environment relationship by allowing measurement outside the laboratory. WIS systems should be validated for team sports movements before they are used in sports performance and injury prevention research. The aim of the present study was to investigate the concurrent validity of a wearable inertial sensor system in quantifying joint kinematics during team sport movements. Ten recreationally active participants performed change-of-direction (single-leg deceleration and sidestep cut) and jump-landing (single-leg hop, single-leg crossover hop, and double-leg vertical jump) tasks while motion was recorded by nine inertial sensors (Noraxon MyoMotion, Noraxon USA Inc.) and eight motion capture cameras (Vicon Motion Systems Ltd). Validity of lower-extremity joint kinematics was assessed using measures of agreement (cross-correlation: XCORR) and error (root mean square deviation; and amplitude difference). Excellent agreement (XCORR >0.88) was found for sagittal plane kinematics in all joints and tasks. Highly variable agreement was found for frontal and transverse plane kinematics at the hip and ankle. Errors were relatively high in all planes. In conclusion, the WIS system provides valid estimates of sagittal plane joint kinematics in team sport movements. However, researchers should correct for offsets when comparing absolute joint angles between systems.

Clinical value of assessing motor performance in postacute stroke patients.

BACKGROUND: Rehabilitative treatment plans after stroke are based on clinical examinations of functional capacity and patient-reported outcomes. Objective information about daily life performance is usually not available, but it may improve therapy personalization. OBJECTIVE: To show that sensor-derived information about daily life performance is clinically valuable for counseling and the planning of rehabilitation programs for individual stroke patients who live at home. Performance information is clinically valuable if it can be used as a decision aid for the therapeutic management or counseling of individual patients. METHODS: This was an observational, cross-sectional case series including 15 ambulatory stroke patients. Motor performance in daily life was assessed with body-worn inertial sensors attached to the wrists, shanks and trunk that estimated basic physical activity and various measures of walking and arm activity in daily life. Stroke severity, motor function and activity, and degree of independence were quantified clinically by standard assessments and patient-reported outcomes. Motor performance was recorded for an average of 5.03 ± 1.1 h on the same day as the clinical assessment. The clinical value of performance information is explored in a narrative style by considering individual patient performance and capacity information. RESULTS: The patients were aged 59.9 ± 9.8 years (mean ± SD), were 6.5 ± 7.2 years post stroke, and had a National Institutes of Health Stroke Score of 4.0 ± 2.6. Capacity and performance measures showed high variability. There were substantial discrepancies between performance and capacity measures in some patients. CONCLUSIONS: This case series shows that information about motor performance in daily life can be valuable for tailoring rehabilitative therapy plans and counseling according to the needs of individual stroke patients. Although the short recording time (average of 5.03 h) limited the scope of the conclusions, this study highlights the usefulness of objective measures of daily life performance for the planning of rehabilitative therapies. Further research is required to investigate whether information about performance in daily life leads to improved rehabilitative therapy results.

Rehabilitation and Return to Sport Assessment after Anterior Cruciate Ligament Injury: Quantifying Joint Kinematics during Complex High-Speed Tasks through Wearable Sensors.

The aim of the present study was to quantify joint kinematics through a wearable sensor system in multidirectional high-speed complex movements used in a protocol for rehabilitation and return to sport assessment after Anterior Cruciate Ligament (ACL) injury, and to validate it against a gold standard optoelectronic marker-based system. Thirty-four healthy athletes were evaluated through a full-body wearable sensor (MTw Awinda, Xsens) and a marker-based optoelectronic (Vicon Nexus, Vicon) system during the execution of three tasks: drop jump, forward sprint, and 90° change of direction. Clinically relevant joint angles of lower limbs and trunk were compared through Pearson's correlation coefficient (r), and the Coefficient of Multiple Correlation (CMC). An excellent agreement (r > 0.94, CMC > 0.96) was found for knee and hip sagittal plane kinematics in all the movements. A fair-to-excellent agreement was found for frontal (r 0.55-0.96, CMC 0.63-0.96) and transverse (r 0.45-0.84, CMC 0.59-0.90) plane kinematics. Movement complexity slightly affected the agreement between the systems. The system based on wearable sensors showed fair-to-excellent concurrent validity in the evaluation of the specific joint parameters commonly used in rehabilitation and return to sport assessment after ACL injury for complex movements. The ACL professionals could benefit from full-body wearable technology in the on-field rehabilitation of athletes.

Classification of Australian football kick types in-situation via ankle-mounted inertial measurement units.

The utility of inertial measurement units (IMUs) for sporting skill and performance analysis during training and competition is advantageous for enhancing the objectivity of athlete monitoring. This study aimed to classify Australian Rules football (AF) kick types in an applied environment using ankle-mounted IMUs. IMUs and video capture of a controlled protocol, including four kick types at varying distances, were recorded during a single testing session with female AF athletes (n = 20). Processed IMU data were modelled using support vector machine classifier, random forest, and k-nearest neighbour algorithms under a 2-Kick, 4-Kick, and kick distance (10, 20, 30 m) conditions. The random forest model showed the highest results for overall classification accuracy (83% 2-Kick and 80% 4-Kick), test F1-score (0.76 2-Kick and 0.81 4-Kick), and AUC score (0.58 2-Kick and 0.60 4-Kick). Kick distance classification showed a model test and class weighted F1-score of 0.63 and overall accuracy of 64%, respectively. This study highlights the potential for an applied semi-automated AF training kick detection and type classification system using IMUs.

Walking With Horizontal Head Turns Is Impaired in Persons With Early-Stage Multiple Sclerosis Showing Normal Locomotion.

BACKGROUND: Turning the head while walking (an action often required during daily living) is particularly challenging to maintain balance. It can therefore potentially reveal subtle impairments in early-stage people with multiple sclerosis who still show normal locomotion (NW-PwMS). This would help in identifying those subjects who can benefit from early preventive exercise aimed at slowing the MS-related functional decline. OBJECTIVES: To analyze if the assessment of walking with horizontal head turns (WHHT) through inertial sensors can discriminate between healthy subjects (HS) and NW-PwMS and between NW-PwMS subgroups. To assess if the discriminant ability of the instrumented WHHT is higher compared to clinical scores. To assess the concurrent validity of the sensor-based metrics. METHODS: In this multicenter study, 40 HS and 59 NW-PwMS [Expanded Disability Status Scale (EDSS) ≤ 2.5, disease duration ≤ 5 years] were tested. Participants executed Item-6 of the Fullerton Advanced Balance scale-short (FAB-s) wearing three inertial sensors on the trunk and ankles. The item required to horizontally turn the head at a beat of the metronome (100 bpm) while walking. Signals of the sensors were processed to compute spatiotemporal, regularity, symmetry, dynamic stability, and trunk sway metrics descriptive of WHHT. RESULTS: Mediolateral regularity, anteroposterior symmetry, and mediolateral stability were reduced in NW-PwMS vs. HS (p ≤ 0.001), and showed moderate discriminant ability (area under the receiver operator characteristic curve [AUC]: 0.71-0.73). AP symmetry and ML stability were reduced (p ≤ 0.026) in EDSS: 2-2.5 vs. EDSS: 0-1.5 subgroup (AUC: 0.69-0.70). The number of NW-PwMS showing at least one abnormal instrumented metric (68%) was larger (p ≤ 0.002) than the number of participants showing abnormal FAB-s-Item6 (32%) and FAB-s clinical scores (39%). EDSS: 2-2.5 subgroup included more individuals showing abnormal instrumented metrics (86%) compared to EDSS: 0-1.5 subgroup (57%). The instrumented metrics significantly correlated with FAB-s-Item6 and FAB-s scores (|Spearman's r s | ≥ 0.37, p < 0.001), thus demonstrating their concurrent validity. CONCLUSION: The instrumented assessment of WHHT provided valid objective metrics that discriminated, with higher sensitivity than clinical scores, between HS and NW-PwMS and between EDSS subgroups. The method is a promising tool to complement clinical evaluation, and reveal subclinical impairments in persons who can benefit from early preventive rehabilitative interventions.

Assessment of Biomechanical Response to Fatigue through Wearable Sensors in Semi-Professional Football Referees.

Quantifying muscle fatigue is a key aspect of everyday sport practice. A reliable and objective solution that can fulfil this task would be deeply important for two main reasons: (i) it would grant an objective indicator to adjust the daily training load for each player and (ii) it would provide an innovative tool to reduce the risk of fatigue-related injuries. Available solutions for objectively quantifying the fatigue level of fatigue can be invasive for the athlete; they could alter the performance or they are not compatible with daily practice on the playground. Building on previous findings that identified fatigue-related parameters in the kinematic of the counter-movement jump (CMJ), this study evaluates the physical response to a fatigue protocol (i.e., Yo-Yo Intermittent Recovery Test Level 1) in 16 football referees, by monitoring CMJ performance with wearable magneto-inertial measurement units (MIMU). Nineteen kinematic parameters were selected as suitable indicators for fatigue detection. The analysis of their variations allowed us to distinguish two opposites but coherent responses to the fatigue protocol. Indeed, eight out of sixteen athletes showed reduced performance (e.g., an effective fatigue condition), while the other eight athletes experienced an improvement of the execution likely due to the so-called Post-Activation Potentiation. In both cases, the above parameters were significantly influenced by the fatigue protocol (p < 0.05), confirming their validity for fatigue monitoring. Interesting correlations between several kinematic parameters and muscular mass were highlighted in the fatigued group. Finally, a "fatigue approximation index" was proposed and validated as fatigue quantifier.

[Effects of the school backpack on walking kinematics: a mechanical overload potentially causing musculoskeletal disorders in developmental age?] / Effetti sulla cinematica del cammino dello zaino scolastico: un sovraccarico meccanico potenzialmente causa di disturbi muscolo-scheletrici in età evolutiva?

SUMMARY: Studies and reviews show that the vast majority of students around the world use heavy and uncomfortable backpacks, which could negatively affect their musculoskeletal development or at least generate a non-physiological functional overload. In this regard, non-invasive analyses were carried out on a sample of 150 healthy students aged between 14 and 15 years using a wearable inertial device for gait analysis: G-Walk System by BTS Bioengineering. Each student performed a gait analysis session consisting in a walk of 15 meters along a straight path in two different conditions: free walk and walk with backpack. A backpack with a sturdy backrest, wide and padded straps and abdominal belt with buckle was chosen. The weight inside the backpack was fixed at 9.3 kg in accordance with scientific studies conducted by Stefano Negrini of ISICO (Istituto Scientifico Italiano Colonna Vertebrale). Aim of this work is to understand, through an accurate analysis both instrumental and statistical, if we can talk about differential influence of musculoskeletal type generated by a school backpack full load compared to no backpack, trying to find out if and how much this affects walking both in terms of space-time parameters and detachment from normality values, and in terms of kinematic parameters such as pelvic rotations angles. Results showed a statistically significant difference between the space-time parameters computed in the two different study conditions, moreover a qualitative and quantitative difference was found for kinematic parameters too, which could imply potential musculoskeletal disorders associated with prolonged and long-lasting use of heavy and uncomfortable backpacks. This study has the ambition to raise awareness of this issue in order to extend legislative limits to the "working" environment of children, that is the school, as it is done for working environments adults (D. lgs 81/08 related to manual maintenance of loads).

Timing estimation for gait in water from inertial sensor measurements: Analysis of the performance of 17 algorithms.

BACKGROUND AND OBJECTIVES: Walking in water is used for rehabilitation in different pathological conditions. For the characterization of gait alterations related to pathology, gait timing assessment is of primary importance. With the widespread use of inertial sensors, several algorithms have been proposed for gait timing estimation (i.e. gait events and temporal parameters) out of the water, while an assessment of their performance for walking in water is still missing. The purpose of the present study was to assess the performance in the temporal segmentation for gait in water of 17 algorithms proposed in the literature. METHODS: Ten healthy volunteers mounting 5 tri-axial inertial sensors (trunk, shanks and feet) walked on dry land and in water. Seventeen different algorithms were implemented and classified based on: 1) sensor position, 2) target variable, and 3) computational approach. Gait events identified from synchronized video recordings were assumed as reference. Temporal parameters were calculated from gait events. Algorithm performance was analysed in terms of sensitivity, positive predictive value, accuracy, and repeatability. RESULTS: For walking in water, all Trunk-based algorithms provided a sensitivity lower than 81% and a positive predictive value lower than 94%, as well as acceleration-based algorithms, independently from sensor location, with the exception of two Shank-based ones. Drop in algorithm sensitivity and positive predictive value was associated to significant differences in the stride pattern of the specific analysed variables during walking in water as compared to walking on dry land, as shown by the intraclass correlation coefficient. When using Shank- or Foot-based algorithms, gait events resulted delayed, but the delay was compensated in the estimate of Stride and Step time; a general underestimation of Stance- and overestimation of Swing-time was observed, with minor exceptions. CONCLUSION: Sensor position, target variable and computational approach determined different error distributions for different gait events and temporal parameters for walking in water. This work supports an evidence-based selection of the most appropriate algorithm for gait timing estimation for walking in water as related to the specific application, and provides relevant information for the design of new algorithms for the specific motor task.

Effect of walking speed during gait in water of healthy elderly.

BACKGROUND: Walking in water (WW) is frequently used as an aquatic exercise in rehabilitation programs for the elderly. Understanding gait characteristics of WW is of primary importance to effectively design specific water-based rehabilitation programs. Moreover, as walking speed in water is reduced with a possible effect on gait parameters, the age- and environment-related changes during WW have to be investigated considering the effects of instantaneous walking speed. RESEARCH QUESTION: how do gait kinematic characteristics differ in healthy elderly between WW and on land walking condition (LW)? Do elderly show different walking patterns compared to young adults? Can these kinematic changes be accounted only by the different environment/age or are they also related to walking speed? METHODS: Nine healthy elderly participants (73.5 ±â€¯5.8 years) were acquired during walking in WW and LW at two different speeds. Kinematic parameters were assessed with waterproofed inertial magnetic sensors using a validated protocol. The influence of environment, age and walking speed on gait parameters was investigated with linear mixed models. RESULTS: Shorter stride distances and longer stride durations were observed in WW compared to LW. In the sagittal plane, hip and knee joint showed larger flexion in WW (>10deg over the whole stride and ∼28deg at foot strike, respectively). Furthermore, lower walking speeds and stride distances were observed in elderly compared to young adults. In the sagittal plane, a slightly more flexed hip joint and a less plantarflexed ankle joint (∼9 deg) were observed in the elderly. SIGNIFICANCE: The results showed the importance of assessing the walking speed during WW, as gait parameters can vary not only for the effect environment but also due to different walking speeds.

Wearable Inertial Sensors for Range of Motion Assessment.

This paper presents the design and development of wearable inertial sensors (WIS) for real-time simultaneous triplanar motion capture of the upper extremity (UE). The sensors simultaneously capture in the frontal, sagittal, and horizontal planes UE range of motion (ROM), which is critical to assess an individual's movement limitations and determine appropriate rehabilitative treatments. Off-the-shelf sensors and microcontrollers are used to develop the WIS system, which wirelessly streams real-time joint orientation for UE ROM measurement. Key developments include: 1) two novel approaches, using earth's gravity (EG approach) and magnetic field (EGM approach) as references, to correct misalignments in the orientation between the sensor and its housing to minimize measurement errors; 2) implementation of the joint coordinate system (JCS)-based method for triplanar ROM measurements for clinical use; and 3) an in-situ guided mounting technique for accurate sensor placement and alignment on human body. The results 1) compare computational time between two orientation misalignment correction approaches (EG approach = 325.05 µs and EGM approach = 92.05µs); 2) demonstrate the accuracy and repeatability of measurements from the WIS system (percent deviation of measured angle from applied angle is less than ±6.5% and percent coefficient of variation is less than 11%, indicating acceptable accuracy and repeatability, respectively); and 3) demonstrate the feasibility of using the WIS system within the JCS framework for providing anatomically-correct simultaneous triplanar ROM measurements of shoulder, elbow, and forearm movements during several upper limb exercises.