Enhancing fall risk assessment: instrumenting vision with deep learning during walks.

BACKGROUND: Falls are common in a range of clinical cohorts, where routine risk assessment often comprises subjective visual observation only. Typically, observational assessment involves evaluation of an individual's gait during scripted walking protocols within a lab to identify deficits that potentially increase fall risk, but subtle deficits may not be (readily) observable. Therefore, objective approaches (e.g., inertial measurement units, IMUs) are useful for quantifying high resolution gait characteristics, enabling more informed fall risk assessment by capturing subtle deficits. However, IMU-based gait instrumentation alone is limited, failing to consider participant behaviour and details within the environment (e.g., obstacles). Video-based eye-tracking glasses may provide additional insight to fall risk, clarifying how people traverse environments based on head and eye movements. Recording head and eye movements can provide insights into how the allocation of visual attention to environmental stimuli influences successful navigation around obstacles. Yet, manual review of video data to evaluate head and eye movements is time-consuming and subjective. An automated approach is needed but none currently exists. This paper proposes a deep learning-based object detection algorithm (VARFA) to instrument vision and video data during walks, complementing instrumented gait. METHOD: The approach automatically labels video data captured in a gait lab to assess visual attention and details of the environment. The proposed algorithm uses a YoloV8 model trained on with a novel lab-based dataset. RESULTS: VARFA achieved excellent evaluation metrics (0.93 mAP50), identifying, and localizing static objects (e.g., obstacles in the walking path) with an average accuracy of 93%. Similarly, a U-NET based track/path segmentation model achieved good metrics (IoU 0.82), suggesting that the predicted tracks (i.e., walking paths) align closely with the actual track, with an overlap of 82%. Notably, both models achieved these metrics while processing at real-time speeds, demonstrating efficiency and effectiveness for pragmatic applications. CONCLUSION: The instrumented approach improves the efficiency and accuracy of fall risk assessment by evaluating the visual allocation of attention (i.e., information about when and where a person is attending) during navigation, improving the breadth of instrumentation in this area. Use of VARFA to instrument vision could be used to better inform fall risk assessment by providing behaviour and context data to complement instrumented e.g., IMU data during gait tasks. That may have notable (e.g., personalized) rehabilitation implications across a wide range of clinical cohorts where poor gait and increased fall risk are common.

Skilled reaching test for shoulder function assessment in a rat model of rotator cuff tear: a pilot study.

BACKGROUND: Functional assessments are crucial to evaluate treatment outcomes in clinical and animal studies on rotator cuff injuries. While gait analysis is commonly used to assess animal models of rotator cuff tears, it is less relevant for human patients as the human shoulder is typically assessed in a non-weight-bearing condition. The present study introduces the skilled reaching test as a shoulder functional assessment tool for rats, which allows for evaluation without weight bearing. METHODS: In the control group, 8 male Sprague-Dawley rats received rotator cuff tear surgery without repair. In the rotator cuff repair group, 20 rats received rotator cuff repair at 4 weeks post rotator cuff tear. For the skilled reaching test, rats were trained to extend their forelimbs to fetch food pellets, and the number of trials, number of attempts and the success rate were recorded. The gait analysis and skilled reaching test were performed at baseline, 4 weeks post-tear, 1, 2, 4, and 8 weeks post-repair. The repeated measures analysis of variance was used to evaluate the effects of time on the shoulder function. The significance level was set at 0.05. RESULTS: The skilled reaching test required 216 h to conduct, while the gait analysis took 44 h. In the rotator cuff repair group, gait performance significantly deteriorated at 1 week post-repair and restored to 4 weeks post-tear levels at 4 weeks post-repair. Regarding the skilled reaching test, the number of attempts, number of trials and the success rate decreased at 1 week post-repair. Subsequently, there was a brief rebound in performance observed at 2 weeks post-repair, followed by a continued decline in the number of attempts and trials. By 8 weeks post-repair, only the success rate had restored to levels similar to those observed at 4 weeks post-tear. CONCLUSION: The skilled reaching test can detect functional deficiencies following rotator cuff tear and repair, while it requires high time and labour costs.

Meta-analysis of the quantitative assessment of lower extremity motor function in elderly individuals based on objective detection.

OBJECTIVE: To avoid deviation caused by the traditional scale method, the present study explored the accuracy, advantages, and disadvantages of different objective detection methods in evaluating lower extremity motor function in elderly individuals. METHODS: Studies on lower extremity motor function assessment in elderly individuals published in the PubMed, Web of Science, Cochrane Library and EMBASE databases in the past five years were searched. The methodological quality of the included trials was assessed using RevMan 5.4.1 and Stata, followed by statistical analyses. RESULTS: In total, 19 randomized controlled trials with a total of 2626 participants, were included. The results of the meta-analysis showed that inertial measurement units (IMUs), motion sensors, 3D motion capture systems, and observational gait analysis had statistical significance in evaluating the changes in step velocity and step length of lower extremity movement in elderly individuals (P < 0.00001), which can be used as a standardized basis for the assessment of motor function in elderly individuals. Subgroup analysis showed that there was significant heterogeneity in the assessment of step velocity [SMD=-0.98, 95%CI(-1.23, -0.72), I2 = 91.3%, P < 0.00001] and step length [SMD=-1.40, 95%CI(-1.77, -1.02), I2 = 86.4%, P < 0.00001] in elderly individuals. However, the sensors (I2 = 9%, I2 = 0%) and 3D motion capture systems (I2 = 0%) showed low heterogeneity in terms of step velocity and step length. The sensitivity analysis and publication bias test demonstrated that the results were stable and reliable. CONCLUSION: observational gait analysis, motion sensors, 3D motion capture systems, and IMUs, as evaluation means, play a certain role in evaluating the characteristic parameters of step velocity and step length in lower extremity motor function of elderly individuals, which has good accuracy and clinical value in preventing motor injury. However, the high heterogeneity of observational gait analysis and IMUs suggested that different evaluation methods use different calculation formulas and indicators, resulting in the failure to obtain standardized indicators in clinical applications. Thus, multimodal quantitative evaluation should be integrated.

Analyzing Dual-Task Paradigms to Improve Postconcussion Assessment and Management.

CONTEXT: Dual-task (simultaneous cognitive-motor activities) assessments have been adapted into reliable and valid clinical concussion measures. However, abundant motor and cognitive variations leave researchers and clinicians uncertain about which combinations elicit the intended dual-task effect. Our objective was to examine differences between commonly employed dual-task motor and cognitive combinations among healthy, college-aged individuals. DESIGN: Cross-sectional laboratory study. METHODS: Twenty participants (age: 21.3 [2.4] y; height: 176.0 [9.1] cm; mass: 76.0 [16.4] kg; 20% with concussion history) completed 4 motor tasks (gait, tandem gait, single-leg balance, and tandem balance) under 5 cognitive conditions (single task, subtraction, month reversal, spelling backward, and visual Stroop) in a research laboratory. The motor performance outcomes were spatiotemporal variables for gait and tandem gait and center of pressure path length (in centimeters) for single-leg and tandem balance. Cognitive outcomes were response rate (responses/second) and cognitive accuracy. We used separate repeated-measures analyses of variance for each motor and cognitive outcome with post hoc Tukey t tests. RESULTS: Gait velocity, gait stride length, and tandem gait velocity demonstrated significant cognitive-motor interactions (P's < .001) such that all dual-task conditions resulted in varyingly slower or shorter movement than single task. Conversely, single-leg balance (P = .627) and tandem balance (P = .434) center of pressure path length did not significantly differ among the dual-task cognitive conditions or relative to single task. Statistically significant cognitive-motor interactions were observed only for spelling backward accuracy (P = .004) and response rates for spelling backward, month reversal, and visual Stroop (P's < .001) such that worse accuracy, but faster response rates, occurred during motor tasks. CONCLUSIONS: Gait and tandem gait motor tasks accompanied with spelling backward or subtraction cognitive tasks demonstrated consistently strong dual-task effects and, therefore, may be the best suited for clinical and research use following concussion.

Rethinking Exoskeleton Simulation-Based Design: The Effect of Using Different Cost Functions.

Designing an exoskeleton that can improve user capabilities is a challenging task, and most designs rely on experiments to achieve this goal. A different approach is to use simulation-based designs to determine optimal device parameters. Most of these simulations use full trajectory tracking limb kinematics during a natural gait as a reference. However, exoskeletons typically change the natural gait kinematics of the user. Other types of simulations assume that human gait is optimized for a cost function that combines several objectives, such as the cost of transport, injury prevention, and stabilization. In this study, we use a 2D OpenSim model consisting of 10 degrees of freedom and considering 18 muscles, together with the Moco optimization tool, to investigate the differences between these two approaches with respect to running with a passive knee exoskeleton. Utilizing this model, we test the effect of a full trajectory tracking objective with different weights (representing the importance of the objective in the optimization cost function) and show that when using weights that are typically used in the literature, there is no deviation from the experimental data. Next, we develop a multi-objective cost function with foot clearance term based on peak knee angle during swing, that achieves trajectories similar (RMSE=7.4 deg) to experimental running data. Finally, we investigate the effect of different parameters in the design of a clutch-based passive knee exoskeleton (1.5 kg at each leg) and find that a design that utilizes a 2.5 Nm/deg spring achieves an improvement of up to 8% in net metabolic energy. Our results show that tracking objectives in the cost function, even with a low weight, hinders the simulation's ability to change the gait trajectory. Thus, there is a need for other predictive simulation methods for exoskeletons.

Motor assessment of X-linked dystonia parkinsonism via machine-learning-based analysis of wearable sensor data.

X-linked dystonia parkinsonism (XDP) is a neurogenetic combined movement disorder involving both parkinsonism and dystonia. Complex, overlapping phenotypes result in difficulties in clinical rating scale assessment. We performed wearable sensor-based analyses in XDP participants to quantitatively characterize disease phenomenology as a potential clinical trial endpoint. Wearable sensor data was collected from 10 symptomatic XDP patients and 3 healthy controls during a standardized examination. Disease severity was assessed with the Unified Parkinson's Disease Rating Scale Part 3 (MDS-UPDRS) and Burke-Fahn-Marsden dystonia scale (BFM). We collected sensor data during the performance of specific MDS-UPDRS/BFM upper- and lower-limb motor tasks, and derived data features suitable to estimate clinical scores using machine learning (ML). XDP patients were at varying stages of disease and clinical severity. ML-based algorithms estimated MDS-UPDRS scores (parkinsonism) and dystonia-specific data features with a high degree of accuracy. Gait spatio-temporal parameters had high discriminatory power in differentiating XDP patients with different MDS-UPDRS scores from controls, XDP freezing of gait, and dystonic/non-dystonic gait. These analyses suggest the feasibility of using wearable sensor data for deriving reliable clinical score estimates associated with both parkinsonian and dystonic features in a complex, combined movement disorder and the utility of motion sensors in quantifying clinical examination.

Development of a Low-Cost Markerless Optical Motion Capture System for Gait Analysis and Anthropometric Parameter Quantification.

Technological advancements have expanded the range of methods for capturing human body motion, including solutions involving inertial sensors (IMUs) and optical alternatives. However, the rising complexity and costs associated with commercial solutions have prompted the exploration of more cost-effective alternatives. This paper presents a markerless optical motion capture system using a RealSense depth camera and intelligent computer vision algorithms. It facilitates precise posture assessment, the real-time calculation of joint angles, and acquisition of subject-specific anthropometric data for gait analysis. The proposed system stands out for its simplicity and affordability in comparison to complex commercial solutions. The gathered data are stored in comma-separated value (CSV) files, simplifying subsequent analysis and data mining. Preliminary tests, conducted in controlled laboratory environments and employing a commercial MEMS-IMU system as a reference, revealed a maximum relative error of 7.6% in anthropometric measurements, with a maximum absolute error of 4.67 cm at average height. Stride length measurements showed a maximum relative error of 11.2%. Static joint angle tests had a maximum average error of 10.2%, while dynamic joint angle tests showed a maximum average error of 9.06%. The proposed optical system offers sufficient accuracy for potential application in areas such as rehabilitation, sports analysis, and entertainment.

Assessment of Gait Patterns during Crutch Assisted Gait through Spatial and Temporal Analysis.

The use of crutches is a common method of assisting people during recovery from musculoskeletal injuries in the lower limbs. There are several different ways to walk with crutches depending on the patient's needs. The structure of crutch gaits or crutch gait patterns varies based on the delay between the aid and foot placement, the number of concurrent points of contact, and laterality. In a rehabilitation process, the prescribed pattern may differ according to the injury, the treatment and the individual's condition. Clinicians may improve diagnosis, assessment, training, and treatment by monitoring and analyzing gait patterns. This study aimed to assess and characterize four crutch walking patterns using spatial and temporal parameters obtained from the instrumented crutches. For this purpose, 27 healthy users performed four different gait patterns over multiple trials. Each trial was recorded using a portable system integrated into the crutches, which measured force, position, and acceleration. Based on the data angle, an algorithm was developed to segment the trials into gait cycles and identify gait phases. The next step was to determine the most appropriate metrics to describe each gait pattern. Several metrics were used to analyze the collected data, including force, acceleration, angle, and stride time. Among 27 participants, significant differences were found between crutch gait patterns. Through the use of these spatial and temporal parameters, promising results were obtained for monitoring assisted gait with crutches. Furthermore, the results demonstrated the possibility of using instrumented crutches as a clinical tool.

Development of sarcopenia assessment system using balance and gait ability: Preliminary tests in the elderly.

BACKGROUND: Sarcopenia is a disease that has recently become an issue, and research on various assessment methods is being conducted based on guidelines published for the diagnosis of sarcopenia. However, most assessments are complex because the inspector must measure each device to collect data, or the elderly must directly manipulate and get assessment. OBJECTIVE: In this study, we developed an sarcopenia assessment system to assess and analyze various sarcopenia in one system. METHODS: The system consists of a scaffold sensor for balance ability assessment, a chair sensor for assessing the walking and lower extremity function, and a hand-held dynamometer for assessing grip strength. The balance pad consists of 326 FSR (Force Sensitive Resistor) sensors, and the accuracy is 10 kg/± 10%. The chair sensor applied four weight sensors (Loadcells) to the corner of the hip of the chair, and the accuracy is up to 150 kg ± 10%, and the resolution is 1 kg ± 10%. All data is transmitted to the software through a USB cable to assess sarcopenia through data analysis. The software has built a database to manage user-specific data. The assessment items consisted of "Short Physical Performance Battery (SPPB) Test" and "Hand Grip Strength Test." After completion, the software automatically terminates the assessment results and classifies sarcopenia according to the presented reference value. RESULTS: The average value of the total SPPB test scores, including the balance test, gait speed test, and chair stand test, of all participants was 9.06 ± 1.97. The average values of the balance test, gait speed test, and chair stand test scores of all participants were 8.48 ± 2.80 sec (score 3.28 ± 1.23), 1.05 ± 0.26 m/sec (score 3.69 ± 0.79), and 16.05 ± 4.62 sec (score 2.08 ± 1.13). The average value of the hand grip test of all participants was 28.57 ± 2.94 kgf. CONCLUSIONS: A preliminary assessment was conducted on the elderly in their 60 s or older with the developed system, and its performance confirmed that the assessment and result analysis were conducted well without any errors in software or hardware. Future studies intend to verify the reliability and accuracy of the assessment results compared to the existing direct measurement methods. In addition, we would like to conduct a study to establish data on the muscle reduction index by age group of Koreans.

Estimating the mechanical cost of transport in human walking with a simple kinematic data-driven mechanical model.

This work utilizes a simplified, streamlined approach to study the mechanical cost of transport in human walking. Utilizing the kinematic motion data of the center of mass, velocities and accelerations are determined using kinematic analysis; the applied force is then obtained using inverse dynamics. We calculate the mechanical cost of transport per step from both synthetic and measured data, using a very simple mechanical model of walking. The approach studied can serve as an informative gait characteristic to monitor rehabilitation in human walking.

Beyond gait speed: exploring the added value of Inertial Measurement Unit-based measurements of gait in the estimation of the walking ability in daily life.

BACKGROUND: Gait speed is often used to estimate the walking ability in daily life in people after stroke. While measuring gait with inertial measurement units (IMUs) during clinical assessment yields additional information, it remains unclear if this information can improve the estimation of the walking ability in daily life beyond gait speed. OBJECTIVE: We evaluated the additive value of IMU-based gait features over a simple gait-speed measurement in the estimation of walking ability in people after stroke. METHODS: Longitudinal data during clinical stroke rehabilitation were collected. The assessment consisted of two parts and was administered every three weeks. In the first part, participants walked for two minutes (2MWT) on a fourteen-meter path with three IMUs attached to low back and feet, from which multiple gait features, including gait speed, were calculated. The dimensionality of the corresponding gait features was reduced with a principal component analysis. In the second part, gait was measured for two consecutive days using one ankle-mounted IMU. Next, three measures of walking ability in daily life were calculated, including the number of steps per day, and the average and maximal gait speed. A gait-speed-only Linear Mixed Model was used to estimate the association between gait speed and each of the three measures of walking ability. Next, the principal components (PC), derived from the 2MWT, were added to the gait-speed-only model to evaluate if they were confounders or effect modifiers. RESULTS: Eighty-one participants were measured during rehabilitation, resulting in 198 2MWTs and 135 corresponding walking-performance measurements. 106 Gait features were reduced to nine PCs with 85.1% explained variance. The linear mixed models demonstrated that gait speed was weakly associated with the average and maximum gait speed in daily life and moderately associated with the number of steps per day. The PCs did not considerably improve the outcomes in comparison to the gait speed only models. CONCLUSIONS: Gait in people after stroke assessed in a clinical setting with IMUs differs from their walking ability in daily life. More research is needed to determine whether these discrepancies also occur in non-laboratory settings, and to identify additional non-gait factors that influence walking ability in daily life.

The work to swing limbs in humans versus chimpanzees and its relation to the metabolic cost of walking.

Compared to their closest ape relatives, humans walk bipedally with lower metabolic cost (C) and less mechanical work to move their body center of mass (external mechanical work, WEXT). However, differences in WEXT are not large enough to explain the observed lower C: humans may also do less work to move limbs relative to their body center of mass (internal kinetic mechanical work, WINT,k). From published data, we estimated differences in WINT,k, total mechanical work (WTOT), and efficiency between humans and chimpanzees walking bipedally. Estimated WINT,k is ~ 60% lower in humans due to changes in limb mass distribution, lower stride frequency and duty factor. When summing WINT,k to WEXT, between-species differences in efficiency are smaller than those in C; variations in WTOT correlate with between-species, but not within-species, differences in C. These results partially support the hypothesis that the low cost of human walking is due to the concerted low WINT,k and WEXT.

Cost function criteria using muscle synergies: Exploring the potential of muscle synergy hypothesis.

BACKGROUND AND OBJECTIVE: Solving the redundant optimization problem for human muscles depends on the cost function. Choosing the appropriate cost function helps to address a specific problem. Muscle synergies are currently limited to those obtained by electromyography. Furthermore, debate continues regarding whether muscle synergy is derived or real. This study proposes new cost functions based on the muscle synergy hypothesis for solving the optimal muscle force output problem through musculoskeletal modeling. METHODS: We propose two new computational cost functions involving muscle synergies, which are extracted from muscle activations predicted by musculoskeletal modelling rather than electromyography. In this study, we constructed a musculoskeletal model for simulation using the "Grand Challenge Competition to Predict In Vivo Knee Loads" dataset. Muscle synergies were obtained using non-negative matrix factorization. Two cost functions with muscle synergies were constructed by integrating the polynomial and min/max criterion. Two new functions were verified and validated in normal, smooth, and bouncy gaits. RESULTS: The muscle synergies based on normal gaits were classified into four modules. The cosine similarities of the first three modules were all >0.9. In the normal and smooth gaits, the forces in most muscles predicted using the two new functions were within three standard deviations of the root mean square error for electromyographic comparisons. Predicted muscle force curves using the four methods as well as characteristic points (i.e., time points in the gait cycle when the significant difference was observed between normal and bouncy gaits) were obtained to validate their predictive capabilities. CONCLUSIONS: This study constructed two new cost functions involving muscle synergies, verified and validated the ability, and explored the potential of muscle synergy hypothesis.

The Difference in the Assessment of Knee Extension/Flexion Angles during Gait between Two Calibration Methods for Wearable Goniometer Sensors.

Frontal and axial knee motion can affect the accuracy of the knee extension/flexion motion measurement using a wearable goniometer. The purpose of this study was to test the hypothesis that calibrating the goniometer on an individual's body would reduce errors in knee flexion angle during gait, compared to bench calibration. Ten young adults (23.2 ± 1.3 years) were enrolled. Knee flexion angles during gait were simultaneously assessed using a wearable goniometer sensor and an optical three-dimensional motion analysis system, and the absolute error (AE) between the two methods was calculated. The mean AE across a gait cycle was 2.4° (0.5°) for the on-body calibration, and the AE was acceptable (<5°) throughout a gait cycle (range: 1.5-3.8°). The mean AE for the on-bench calibration was 4.9° (3.4°) (range: 1.9-13.6°). Statistical parametric mapping (SPM) analysis revealed that the AE of the on-body calibration was significantly smaller than that of the on-bench calibration during 67-82% of the gait cycle. The results indicated that the on-body calibration of a goniometer sensor had acceptable and better validity compared to the on-bench calibration, especially for the swing phase of gait.

Machine learning-based bioimpedance assessment of knee osteoarthritis severity.

This study proposes a multiclass model to classify the severity of knee osteoarthritis (KOA) using bioimpedance measurements. The experimental setup considered three types of measurements using eight electrodes: global impedance with adjacent pattern, global impedance with opposite pattern, and direct impedance measurement, which were taken using an electronic device proposed by authors and based on the Analog Devices AD5933 impedance converter. The study comprised 37 participants, 25 with healthy knees and 13 with three different degrees of KOA. All participants performed 20 repetitions of each of the following five tasks: (i) sitting with the knee bent, (ii) sitting with the knee extended, (iii) sitting and performing successive extensions and flexions of the knee, (iv) standing, and (v) walking. Data from the 15 experimental setups (3 types of measurements×5 exercises) were used to train a multiclass random forest. The training and validation cycle was repeated 100 times using random undersampling. At each of the 100 cycles, 80% of the data were used for training and the rest for testing. The results showed that the proposed approach achieved average sensitivities and specificities of 100% for the four KOA severity grades in the extension, cyclic, and gait tasks. This suggests that the proposed method can serve as a screening tool to determine which individuals should undergo x-rays or magnetic resonance imaging for further evaluation of KOA.

BlazePose-Seq2Seq: Leveraging Regular RGB Cameras for Robust Gait Assessment.

Evaluation of human gait through smartphone-based pose estimation algorithms provides an attractive alternative to costly lab-bound instrumented assessment and offers a paradigm shift with real time gait capture for clinical assessment. Systems based on smart phones, such as OpenPose and BlazePose have demonstrated potential for virtual motion assessment but still lack the accuracy and repeatability standards required for clinical viability. Seq2seq architecture offers an alternative solution to conventional deep learning techniques for predicting joint kinematics during gait. This study introduces a novel enhancement to the low-powered BlazePose algorithm by incorporating a Seq2seq autoencoder deep learning model. To ensure data accuracy and reliability, synchronized motion capture involving an RGB camera and ten Vicon cameras were employed across three distinct self-selected walking speeds. This investigation presents a groundbreaking avenue for remote gait assessment, harnessing the potential of Seq2seq architectures inspired by natural language processing (NLP) to enhance pose estimation accuracy. When comparing BlazePose alone to the combination of BlazePose and 1D convolution Long Short-term Memory Network (1D-LSTM), Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM), the average mean absolute errors decreased from 13.4° to 5.3° for fast gait, from 16.3° to 7.5° for normal gait, and from 15.5° to 7.5° for slow gait at the left ankle joint angle respectively. The strategic utilization of synchronized data and rigorous testing methodologies further bolsters the robustness and credibility of these findings.

[A study of cognitive impairment quantitative assessment method based on gait characteristics].

Alzheimer's disease (AD) is a common and serious form of elderly dementia, but early detection and treatment of mild cognitive impairment can help slow down the progression of dementia. Recent studies have shown that there is a relationship between overall cognitive function and motor function and gait abnormalities. We recruited 302 cases from the Rehabilitation Hospital Affiliated to National Rehabilitation Aids Research Center and included 193 of them according to the screening criteria, including 137 patients with MCI and 56 healthy controls (HC). The gait parameters of the participants were collected during performing single-task (free walking) and dual-task (counting backwards from 100) using a wearable device. By taking gait parameters such as gait cycle, kinematics parameters, time-space parameters as the focus of the study, using recursive feature elimination (RFE) to select important features, and taking the subject's MoCA score as the response variable, a machine learning model based on quantitative evaluation of cognitive level of gait features was established. The results showed that temporal and spatial parameters of toe-off and heel strike had important clinical significance as markers to evaluate cognitive level, indicating important clinical application value in preventing or delaying the occurrence of AD in the future.

Differences in running technique between runners with better and poorer running economy and lower and higher milage: An artificial neural network approach.

BACKGROUND: Prior studies investigated selected discrete sagittal-plane outcomes (e.g., peak knee flexion) in relation to running economy, hereby discarding the potential relevance of running technique parameters during noninvestigated phases of the gait cycle and in other movement planes. PURPOSE: Investigate which components of running technique distinguish groups of runners with better and poorer economy and higher and lower weekly running distance using an artificial neural network (ANN) approach with layer-wise relevance propagation. METHODS: Forty-one participants (22 males and 19 females) ran at 2.78 m∙s-1 while three-dimensional kinematics and gas exchange data were collected. Two groups were created that differed in running economy or weekly training distance. The three-dimensional kinematic data were used as input to an ANN to predict group allocations. Layer-wise relevance propagation was used to determine the relevance of three-dimensional kinematics for group classification. RESULTS: The ANN classified runners in the correct economy or distance group with accuracies of up to 62% and 71%, respectively. Knee, hip, and ankle flexion were most relevant to both classifications. Runners with poorer running economy showed higher knee flexion during swing, more hip flexion during early stance, and more ankle extension after toe-off. Runners with higher running distance showed less trunk rotation during swing. CONCLUSION: The ANN accuracy was moderate when predicting whether runners had better, or poorer running economy, or had a higher or lower weekly training distance based on their running technique. The kinematic components that contributed the most to the classification may nevertheless inform future research and training.

Assessment of Sensorized Insoles in Balance and Gait in Individuals With Parkinson's Disease.

Individuals with Parkinson's disease (PD) are characterized by gait and balance disorders limiting their independence and quality of life. Home-based rehabilitation programs, combined with drug therapy, demonstrated to be beneficial in the daily-life activities of PD subjects. Sensorized shoes can extract balance- and gait-related data in home-based scenarios and allow clinicians to monitor subjects' activities. In this study, we verified the capability of a pair of sensorized shoes (including pressure-sensitive insoles and one inertial measurement unit) in assessing ground-level walking and body weight shift exercises. The shoes can potentially be combined with a sensory biofeedback module that provides vibrotactile cues to individuals. Sensorized shoes have been assessed in terms of the capability of detecting relevant gait events (heel strike, flat foot, toe off), estimating spatiotemporal parameters of gait (stance, swing, and double support duration, stride length), estimating gait variables (vertical ground-reaction force, vGRF; coordinate of the center of pressure along the longitudinal axes of the feet, yCoP; and the dorsiflexion angle of the feet, Pitch angle). The assessment compared the outcomes with those extracted from the gold standard equipment, namely force platforms and a motion capture system. Results of this comparison with 9 PD subjects showed an overall median absolute error lower than 0.03 s in detecting the foot-contact, foot-off, and heel-off gait events while performing ground-level walking and lower than 0.15 s in body weight shift exercises. The computation of spatiotemporal parameters of gait showed median errors of 1.62 % of the stance phase duration and 0.002 m of the step length. Regarding the estimation of vGRF, yCoP, and Pitch angle, the median across-subjects Pearson correlation coefficient was 0.90, 0.94, and 0.91, respectively. These results confirm the suitability of the sensorized shoes for quantifying biomechanical features during body weight shift and gait exercises of PD and pave the way to exploit the biofeedback modules of the bidirectional interface in future studies.

Reconsidering the Gold Standard for the Assessment of Spastic Equinus Foot.

INTRODUCTION: To determine whether differences exist in the measured range of motion (ROM) by the goniometer, equinometer, and 3-dimensional (3D) gait analysis in different settings (under general anesthesia (GA) or in the clinic) among patients with cerebral palsy. METHODS: A prospective comparative cohort study was conducted at our orthopaedics center, where 15 patients with diplegic cerebral palsy (30 limbs) were evaluated, all of whom had Gross Motor Function Classification System (GMFCS) level I and II. ROM was measured by (1) goniometer under GA (benchmark), (2) goniometer analysis during clinical examination, (3) equinometer under GA, (4) equinometer in the clinic, and (5) 3D gait analysis. ROM was measured during both knee flexion and extension. Subgroup analysis based on the GMFCS level was performed. RESULTS: Nine patients were male with GMFCS level I and a mean age of 14.3 (SD=7.2) years. Statistically significant differences were noted between all studied measurement techniques in terms of ROM, dorsiflexion, and plantarflexion during both knee flexion and extension. The GMFCS level was an effect modifier of ROM measurements. DISCUSSION: The ROM parameters during both knee flexion and extension differed from one measurement device/technique to another. The GMFCS level played a notable effect-modifying role on the ROM parameters.