A model of task-level human stepping regulation yields semistable walking.

A simple lateral dynamic walker, with swing leg dynamics and three adjustable input parameters, is used to study how motor regulation affects frontal-plane stepping. Motivated by experimental observations and phenomenological models, we imposed task-level multi-objective regulation targeting the walker's optimal lateral foot placement at each step. The regulator prioritizes achieving step width and lateral body position goals to varying degrees by choosing a mixture parameter. Our model thus integrates a lateral mechanical template, which captures the fundamental mechanics of frontal-plane walking, with a lateral motor regulation template, an empirically verified model of how humans manipulate lateral foot placements in a goal-directed manner. The model captures experimentally observed stepping fluctuation statistics and demonstrates how linear empirical models of stepping dynamics can emerge from first-principles nonlinear mechanics. We find that task-level regulation gives rise to a goal-equivalent manifold in the system's extended state space of mechanical states and inputs, a subset of which contains a continuum of period-1 gaits forming a semistable set: perturbations off of any of its gaits result in transients that return to the set, though typically to different gaits.

Efficacy of tissue-bone homeostasis manipulation on the gait and knee function for the patients with knee osteoarthritis: a randomized controlled trial.

BACKGROUND: Knee osteoarthritis (KOA) was characterized by pain and limited joint function, which seriously affected the quality of life of patients. The vast majority of KOA was closely related to degeneration of the patellofemoral joint and abnormal patellar movement trajectory. Tissue-bone homeostasis manipulation (TBHM) could correct abnormal patellar movement trajectory on the basis of loosening soft tissue. However, there was little strong evidence to verify its efficacy on the patients with KOA. The study objective was to explore the efficacy of the TBHM on gait and knee function in the patients with KOA. METHODS: Sixty KOA patients were randomly assigned to either the joint mobilization (n = 30) or TBHM (n = 30) group. The joint mobilization group received joint mobilization, while the TBHM group received TBHM. For two groups, the patients participated in 30 min rehabilitation sessions thrice per week for 12 weeks. The primary outcome was biomechanical gait outcomes during walking, including step length, step velocity, double support, knee range of motion (ROM), and knee adduction moment (KAM). The secondary outcomes were the Western Ontario and McMaster Index (WOMAC) and 36-Item short- form health survey (SF-36), which reflected improvements in knee function and quality of life, respectively. At baseline and 12 weeks, evaluations were conducted and compared between groups. RESULTS: After a 12-week intervention, significant group differences were observed in KAM (p = 0.018), WOMAC-Pain (p = 0.043) and WOMAC-Stiffness (p = 0.026). A noteworthy finding was the presence of a significant interaction effect between group and time specifically observed in step velocity during gait (p = 0.046), WOMAC-Function (p = 0.013) and SF-36 (p = 0.027). Further analysis revealed a significant difference in step velocity (p = 0.034), WOMAC-Function (p = 0.025) and SF-36 (p = 0.042) during post-assessment between the two groups. Moreover, a significant time effect was observed across all outcomes of the two groups (p < 0.05). CONCLUSION: The TBHM intervention has better improved the gait, knee function, and quality of life in the patients with KOA. TRIAL REGISTRATION: ITMCTR, ITMCTR2200005507. Registered 06/01/2022, http://itmctr.ccebtcm.org.cn/zh-CN/Home/ProjectView?pid=09cdadad-0aef-41ee-81bd-a8dceb63f7f5 .

Multibody dynamics-based musculoskeletal modeling for gait analysis: a systematic review.

Beyond qualitative assessment, gait analysis involves the quantitative evaluation of various parameters such as joint kinematics, spatiotemporal metrics, external forces, and muscle activation patterns and forces. Utilizing multibody dynamics-based musculoskeletal (MSK) modeling provides a time and cost-effective non-invasive tool for the prediction of internal joint and muscle forces. Recent advancements in the development of biofidelic MSK models have facilitated their integration into clinical decision-making processes, including quantitative diagnostics, functional assessment of prosthesis and implants, and devising data-driven gait rehabilitation protocols. Through an extensive search and meta-analysis of over 116 studies, this PRISMA-based systematic review provides a comprehensive overview of different existing multibody MSK modeling platforms, including generic templates, methods for personalization to individual subjects, and the solutions used to address statically indeterminate problems. Additionally, it summarizes post-processing techniques and the practical applications of MSK modeling tools. In the field of biomechanics, MSK modeling provides an indispensable tool for simulating and understanding human movement dynamics. However, limitations which remain elusive include the absence of MSK modeling templates based on female anatomy underscores the need for further advancements in this area.

Predictive musculoskeletal simulations reveal the mechanistic link between speed, posture and energetics among extant mammals.

An unusual pattern among the scaling laws in nature is that the fastest animals are neither the largest, nor the smallest, but rather intermediately sized. Because of the enormous diversity in animal shape, the mechanisms underlying this have long been difficult to determine. To address this, we challenge predictive human musculoskeletal simulations, scaled in mass from the size of a mouse (0.1 kg) to the size of an elephant (2000 kg), to move as fast as possible. Our models replicate patterns observed across extant animals including: (i) an intermediate optimal body mass for speed; (ii) a reduction in the cost of transport with increasing size; and (iii) crouched postures at smaller body masses and upright postures at larger body masses. Finally, we use our models to determine the mechanical limitations of speed with size, showing larger animals may be limited by their ability to produce muscular force while smaller animals are likely limited by their ability to produce larger ground reaction forces. Despite their bipedal gait, our models replicate patterns observed across quadrupedal animals, suggesting these biological phenomena likely represent general rules and are not the result of phylogenetic or other ecological factors that typically hinder comparative studies.

Exploring unsupervised feature extraction of IMU-based gait data in stroke rehabilitation using a variational autoencoder.

BACKGROUND: Variational AutoEncoders (VAE) might be utilized to extract relevant information from an IMU-based gait measurement by reducing the sensor data to a low-dimensional representation. The present study explored whether VAEs can reduce IMU-based gait data of people after stroke into a few latent features with minimal reconstruction error. Additionally, we evaluated the psychometric properties of the latent features in comparison to gait speed, by assessing 1) their reliability; 2) the difference in scores between people after stroke and healthy controls; and 3) their responsiveness during rehabilitation. METHODS: We collected test-retest and longitudinal two-minute walk-test data using an IMU from people after stroke in clinical rehabilitation, as well as from a healthy control group. IMU data were segmented into 5-second epochs, which were reduced to 12 latent-feature scores using a VAE. The between-day test-retest reliability of the latent features was assessed using ICC-scores. The differences between the healthy and the stroke group were examined using an independent t-test. Lastly, the responsiveness was determined as the number of individuals who significantly changed during rehabilitation. RESULTS: In total, 15,381 epochs from 107 people after stroke and 37 healthy controls were collected. The VAE achieved data reconstruction with minimal errors. Five latent features demonstrated good-to-excellent test-retest reliability. Seven latent features were significantly different between groups. We observed changes during rehabilitation for 21 and 20 individuals in latent-feature scores and gait speed, respectively. However, the direction of the change scores of the latent features was ambiguous. Only eleven individuals exhibited changes in both latent-feature scores and gait speed. CONCLUSION: VAEs can be used to effectively reduce IMU-based gait assessment to a concise set of latent features. Some latent features had a high test-retest reliability and differed significantly between healthy controls and people after stroke. Further research is needed to determine their clinical applicability.

Ability of Heart Rate Recovery and Gait Kinetics in a Single Wearable to Predict Frailty: Quasiexperimental Pilot Study.

BACKGROUND: Aging is a risk factor for falls, frailty, and disability. The utility of wearables to screen for physical performance and frailty at the population level is an emerging research area. To date, there is a limited number of devices that can measure frailty and physical performance simultaneously. OBJECTIVE: The aim of this study is to evaluate the accuracy and validity of a continuous digital monitoring wearable device incorporating gait mechanics and heart rate recovery measurements for detecting frailty, poor physical performance, and falls risk in older adults at risk of falls. METHODS: This is a substudy of 156 community-dwelling older adults ≥60 years old with falls or near falls in the past 12 months who were recruited for a fall prevention intervention study. Of the original participants, 22 participants agreed to wear wearables on their ankles. An interview questionnaire involving demographics, cognition, frailty (FRAIL), and physical function questions as well as the Falls Risk for Older People in the Community (FROP-Com) was administered. Physical performance comprised gait speed, timed up and go (TUG), and the Short Physical Performance Battery (SPPB) test. A gait analyzer was used to measure gait mechanics and steps (FRAIL-functional: fatigue, resistance, and aerobic), and a heart rate analyzer was used to measure heart rate recovery (FRAIL-nonfunctional: weight loss and chronic illness). RESULTS: The participants' mean age was 74.6 years. Of the 22 participants, 9 (41%) were robust, 10 (46%) were prefrail, and 3 (14%) were frail. In addition, 8 of 22 (36%) had at least one fall in the past year. Participants had a mean gait speed of 0.8 m/s, a mean SPPB score of 8.9, and mean TUG time of 13.8 seconds. The sensitivity, specificity, and area under the curve (AUC) for the gait analyzer against the functional domains were 1.00, 0.84, and 0.92, respectively, for SPPB (balance and gait); 0.38, 0.89, and 0.64, respectively, for FRAIL-functional; 0.45, 0.91, and 0.68, respectively, for FROP-Com; 0.60, 1.00, and 0.80, respectively, for gait speed; and 1.00, 0.94, and 0.97, respectively, for TUG. The heart rate analyzer demonstrated superior validity for the nonfunctional components of frailty, with a sensitivity of 1.00, specificity of 0.73, and AUC of 0.83. CONCLUSIONS: Agreement between the gait and heart rate analyzers and the functional components of the FRAIL scale, gait speed, and FROP-Com was significant. In addition, there was significant agreement between the heart rate analyzer and the nonfunctional components of the FRAIL scale. The gait and heart rate analyzers could be used in a screening test for frailty and falls in community-dwelling older adults but require further improvement and validation at the population level.

Automatic gait evoking in healthy adults through Vojta's peripheric somatosensory stimulation: a double-blind randomized controlled trial.

BACKGROUND: To study the effects of different interventions on automatic gait processing in contrast with voluntary gait processing in healthy subjects. METHODS: A double-blind randomised controlled trial was designed (120 able-body persons between 18 and 65 years old entered and completed the study), with pre-intervention and post-intervention assessments using the 6-Minute Walk Test (6MWT). The participants were randomly distributed into four groups. Prior to intervention, all participants performed voluntary gait on the ground (VoG) in a calibrated circuit following the 6MWT. The presence of automatic gait (AG) was explored post-intervention without a voluntary demand in the same circuit following the 6MWT. Each group received a different intervention for 30 min: Vojta stimulation, MOTOMED® at no less than 60 revolutions/minute, treadmill walking at 3 km/h, and resting in a chair (control). The main assessment, conducted by a blinded rater, was the difference in distance covered (in meters) during the 6MWT between pre- and post-intervention. Surface electromyography (sEMG) average root mean square (RMS) signals in the right tibialis anterior, right soleus, right rectus femoris, and right biceps femoris were also considered outcome measures. RESULTS: The Vojta group was the only one that initiated AG after the intervention (476.4 m ± 57.1 in VoG versus 9.0 m ± 8.9 in AG, p < 0.001) with comparable kinematics and EMG parameters during voluntary gait, except for ankle dorsal flexion. Within the Vojta group, high variability in kinematics, sEMG activity, and distance covered was observed. CONCLUSIONS: AG isolation is approachable through Vojta at only one session measurable with the 6MWT without any voluntary gait demand. No automatic gait effects were observed post-intervention in the other groups. TRIAL REGISTRATION: NCT04689841 (ClinicalTrials.gov).

Effect of mediolateral leg perturbations on walking balance in people with chronic stroke: A randomized controlled trial.

Many people with chronic stroke (PwCS) exhibit deficits in step width modulation, an important strategy for walking balance. A single exposure to swing leg perturbations can temporarily strengthen this modulation. The objective of this parallel, double-blinded, randomized controlled trial was to investigate whether repeated perturbations cause sustained increases in step modulation (NCT02964039; funded by the VA). 54 PwCS at the Medical University of South Carolina were randomly assigned to one of three intervention groups: Control (n = 18), with minimal forces; Assistive (n = 18), pushing the swing leg toward a mechanically appropriate location; Perturbing (n = 18), pushing the swing leg away from a mechanically appropriate location. All intervention groups included 24 training sessions over 12-weeks with up to 30-minutes of treadmill walking while interfaced with a novel force-field and a 12-week follow-up period, with five interspersed assessment sessions. Our primary outcome measure was paretic step width modulation, the partial correlation between step width and pelvis displacement (ρSW). Secondarily, we quantified swing and stance leg contributions to step modulation, clinical assessments of walking balance and confidence, and real-world falls. Outcomes were analyzed for participants who completed all assessment sessions (n = 44). Only the Perturbing group exhibited significant increases in paretic ρSW, which were present after 4-weeks of training and sustained through follow-up (t = 2.42-3.17). These changes were due to improved control of paretic swing leg positioning. However, perturbation-induced changes in step modulation were not always significantly greater than those in the Control group, and clinical assessments were similar across intervention groups. Participants in the Perturbing group experienced a lower fall rate than those in the Control group (incidence rate ratio = 0.53), although our small sample size warrants caution. The present results indicate that perturbations can cause sustained modifications of targeted biomechanical characteristics of post-stroke gait, although such changes alone may be insufficient to change more complex clinical assessments.

Muscle-controlled physics simulations of bird locomotion resolve the grounded running paradox.

Humans and birds use very different running styles. Unlike humans, birds adopt "grounded running" at intermediate speeds-a running gait where at least one foot always maintains ground contact. Avian grounded running is a paradox: Animals usually minimize locomotor energy expenditure, but birds prefer grounded running despite incurring higher energy costs. Using predictive gait simulations of the emu (Dromaius novaehollandiae), we resolve this paradox by demonstrating that grounded running represents an optimal gait for birds, from both energetics and muscle excitations perspectives. Our virtual experiments decoupled effects of posture and tendon elasticity, biomechanically relevant anatomical features that cannot be isolated in real birds. The avian body plan prevents (near) vertical leg postures, making the running style used by humans impossible. Under this anatomical constraint, grounded running is optimal if the muscles produce the highest forces in crouched postures, as is true in most birds. Shared anatomical features suggest that, as a behavior, avian grounded running first evolved within non-avian dinosaurs.

Better understanding fall risk: AI-based computer vision for contextual gait assessment.

Contemporary research to better understand free-living fall risk assessment in Parkinson's disease (PD) often relies on the use of wearable inertial-based measurement units (IMUs) to quantify useful temporal and spatial gait characteristics (e.g., step time, step length). Although use of IMUs is useful to understand some intrinsic PD fall-risk factors, their use alone is limited as they do not provide information on extrinsic factors (e.g., obstacles). Here, we update on the use of ergonomic wearable video-based eye-tracking glasses coupled with AI-based computer vision methodologies to provide information efficiently and ethically in free-living home-based environments to better understand IMU-based data in a small group of people with PD. The use of video and AI within PD research can be seen as an evolutionary step to improve methods to understand fall risk more comprehensively.

Sense of embodiment with synchronized avatar during walking in mixed reality.

Gait guidance systems that synchronize the gait rhythm with an avatar in a mixed reality (MR) environment are attracting attention owing to their rehabilitation applications. More effective gait guidance can be achieved by changing body sensations for the sense of embodiment (SoE), which refers to the feeling of owning, controlling, and being inside a body in MR. This study investigated full-body synchronous motion between a human and a virtual avatar to enhance the SoE in walking with actual position changes in the real world. The full-body motion and gait rhythm were measured using body-worn inertial measurement units and a visual avatar was provided through a transparent head-mounted display. The results showed that the SoE of the participants was enhanced under higher synchronization conditions. In addition, questionnaire results showed that the SoE in the synchronous condition was significantly higher than that in the asynchronous condition, and the SoE in the self-avatar condition was significantly higher than that in the other-avatar condition. This indicates that a higher synchronization level with the appearance of an avatar leads to a stronger SoE in the human perception mechanism, which is important for potential application in medical or other fields.

Gait and Balance Assessments with Augmented Reality Glasses in People with Parkinson's Disease: Concurrent Validity and Test-Retest Reliability.

State-of-the-art augmented reality (AR) glasses record their 3D pose in space, enabling measurements and analyses of clinical gait and balance tests. This study's objective was to evaluate concurrent validity and test-retest reliability for common clinical gait and balance tests in people with Parkinson's disease: Five Times Sit To Stand (FTSTS) and Timed Up and Go (TUG) tests. Position and orientation data were collected in 22 participants with Parkinson's disease using HoloLens 2 and Magic Leap 2 AR glasses, from which test completion durations and durations of distinct sub-parts (e.g., sit to stand, turning) were derived and compared to reference systems and over test repetitions. Regarding concurrent validity, for both tests, an excellent between-systems agreement was found for position and orientation time series (ICC(C,1) > 0.933) and test completion durations (ICC(A,1) > 0.984). Between-systems agreement for FTSTS (sub-)durations were all excellent (ICC(A,1) > 0.921). TUG turning sub-durations were excellent (turn 1, ICC(A,1) = 0.913) and moderate (turn 2, ICC(A,1) = 0.589). Regarding test-retest reliability, the within-system test-retest variation in test completion times and sub-durations was always much greater than the between-systems variation, implying that (sub-)durations may be derived interchangeably from AR and reference system data. In conclusion, AR data are of sufficient quality to evaluate gait and balance aspects in people with Parkinson's disease, with valid quantification of test completion durations and sub-durations of distinct FTSTS and TUG sub-parts.

MFCF-Gait: Small Silhouette-Sensitive Gait Recognition Algorithm Based on Multi-Scale Feature Cross-Fusion.

Gait recognition based on gait silhouette profiles is currently a major approach in the field of gait recognition. In previous studies, models typically used gait silhouette images sized at 64 × 64 pixels as input data. However, in practical applications, cases may arise where silhouette images are smaller than 64 × 64, leading to a loss in detail information and significantly affecting model accuracy. To address these challenges, we propose a gait recognition system named Multi-scale Feature Cross-Fusion Gait (MFCF-Gait). At the input stage of the model, we employ super-resolution algorithms to preprocess the data. During this process, we observed that different super-resolution algorithms applied to larger silhouette images also affect training outcomes. Improved super-resolution algorithms contribute to enhancing model performance. In terms of model architecture, we introduce a multi-scale feature cross-fusion network model. By integrating low-level feature information from higher-resolution images with high-level feature information from lower-resolution images, the model emphasizes smaller-scale details, thereby improving recognition accuracy for smaller silhouette images. The experimental results on the CASIA-B dataset demonstrate significant improvements. On 64 × 64 silhouette images, the accuracies for NM, BG, and CL states reached 96.49%, 91.42%, and 78.24%, respectively. On 32 × 32 silhouette images, the accuracies were 94.23%, 87.68%, and 71.57%, respectively, showing notable enhancements.

Analysis of Gait Kinematics in Smart Walker-Assisted Locomotion in Immersive Virtual Reality Scenario.

The decline in neuromusculoskeletal capabilities of older adults can affect motor control, independence, and locomotion. Because the elderly population is increasing worldwide, assisting independent mobility and improving rehabilitation therapies has become a priority. The combination of rehabilitation robotic devices and virtual reality (VR) tools can be used in gait training to improve clinical outcomes, motivation, and treatment adherence. Nevertheless, VR tools may be associated with cybersickness and changes in gait kinematics. This paper analyzes the gait parameters of fourteen elderly participants across three experimental tasks: free walking (FW), smart walker-assisted gait (AW), and smart walker-assisted gait combined with VR assistance (VRAW). The kinematic parameters of both lower limbs were captured by a 3D wearable motion capture system. This research aims at assessing the kinematic adaptations when using a smart walker and how the integration between this robotic device and the VR tool can influence such adaptations. Additionally, cybersickness symptoms were investigated using a questionnaire for virtual rehabilitation systems after the VRAW task. The experimental data indicate significant differences between FW and both AW and VRAW. Specifically, there was an overall reduction in sagittal motion of 16%, 25%, and 38% in the hip, knee, and ankle, respectively, for both AW and VRAW compared to FW. However, no significant differences between the AW and VRAW kinematic parameters and no adverse symptoms related to VR were identified. These results indicate that VR technology can be used in walker-assisted gait rehabilitation without compromising kinematic performance and presenting potential benefits related to motivation and treatment adherence.

The Cumulative Impacts of Fatigue during Overload Training Can Be Tracked Using Field-Based Monitoring of Running Stride Interval Correlations.

Integrating running gait coordination assessment into athlete monitoring systems could provide unique insight into training tolerance and fatigue-related gait alterations. This study investigated the impact of an overload training intervention and recovery on running gait coordination assessed by field-based self-testing. Fifteen trained distance runners were recruited to perform 1-week of light training (baseline), 2 weeks of heavy training (high intensity, duration, and frequency) designed to overload participants, and a 10-day light taper to allow recovery and adaptation. Field-based running assessments using ankle accelerometry and online short recovery and stress scale (SRSS) surveys were completed daily. Running performance was assessed after each training phase using a maximal effort multi-stage running test-to-exhaustion (RTE). Gait coordination was assessed using detrended fluctuation analysis (DFA) of a stride interval time series. Two participants withdrew during baseline training due to changed personal circumstances. Four participants withdrew during heavy training due to injury. The remaining nine participants completed heavy training and were included in the final analysis. Heavy training reduced DFA values (standardised mean difference (SMD) = -1.44 ± 0.90; p = 0.004), recovery (SMD = -1.83 ± 0.82; p less than 0.001), performance (SMD = -0.36 ± 0.32; p = 0.03), and increased stress (SMD = 1.78 ± 0.94; p = 0.001) compared to baseline. DFA values (p = 0.73), recovery (p = 0.77), and stress (p = 0.73) returned to baseline levels after tapering while performance trended towards improvement from baseline (SMD = 0.28 ± 0.37; p = 0.13). Reduced DFA values were associated with reduced performance (r2 = 0.55) and recovery (r2 = 0.55) and increased stress (r2 = 0.62). Field-based testing of running gait coordination is a promising method of monitoring training tolerance in running athletes during overload training.

GaitKeeper: An AI-Enabled Mobile Technology to Standardize and Measure Gait Speed.

Gait speed is increasingly recognized as an important health indicator. However, gait analysis in clinical settings often encounters inconsistencies due to methodological variability and resource constraints. To address these challenges, GaitKeeper uses artificial intelligence (AI) and augmented reality (AR) to standardize gait speed assessments. In laboratory conditions, GaitKeeper demonstrates close alignment with the Vicon system and, in clinical environments, it strongly correlates with the Gaitrite system. The integration of a cloud-based processing platform and robust data security positions GaitKeeper as an accurate, cost-effective, and user-friendly tool for gait assessment in diverse clinical settings.

Machine Learning Based Abnormal Gait Classification with IMU Considering Joint Impairment.

Gait analysis systems are critical for assessing motor function in rehabilitation and elderly care. This study aimed to develop and optimize an abnormal gait classification algorithm considering joint impairments using inertial measurement units (IMUs) and walkway systems. Ten healthy male participants simulated normal walking, walking with knee impairment, and walking with ankle impairment under three conditions: without joint braces, with a knee brace, and with an ankle brace. Based on these simulated gaits, we developed classification models: distinguishing abnormal gait due to joint impairments, identifying specific joint disorders, and a combined model for both tasks. Recursive Feature Elimination with Cross-Validation (RFECV) was used for feature extraction, and models were fine-tuned using support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGB). The IMU-based system achieved over 91% accuracy in classifying the three types of gait. In contrast, the walkway system achieved less than 77% accuracy in classifying the three types of gait, primarily due to high misclassification rates between knee and ankle joint impairments. The IMU-based system shows promise for accurate gait assessment in patients with joint impairments, suggesting future research for clinical application improvements in rehabilitation and patient management.

The Estimation of Knee Medial Force with Substitution Parameters during Walking and Turning.

PURPOSE: Knee adduction, flexion moment, and adduction angle are often used as surrogate parameters of knee medial force. To verify whether these parameters are suitable as surrogates under different walking states, we investigated the correlation between knee medial loading with the surrogates during walking and turning. METHODS: Sixteen healthy subjects were recruited to complete straight walk (SW), step turn (ST), and crossover turn (CT). Knee joint moments were obtained using inverse dynamics, and knee medial force was computed using a previously validated musculoskeletal model, Freebody. Linear regression was used to predict the peak of knee medial force with the peaks of the surrogate parameters and walking speed. RESULTS: There was no significant difference in walking speed among these three tasks. The peak knee adduction moment (pKAM) was a significant predictor of the peak knee medial force (pKMF) for SW, ST, and CT (p < 0.001), while the peak knee flexion moment (pKFM) was only a significant predictor of the pKMF for SW (p = 0.034). The statistical analysis showed that the pKMF increased, while the pKFM and the peak knee adduction angle (pKAA) decreased significantly during CT compared to those of SW and ST (p < 0.001). The correlation analysis indicated that the knee parameters during SW and ST were quite similar. CONCLUSIONS: This study investigated the relationship between knee medial force and some surrogate parameters during walking and turning. KAM was still the best surrogate parameter for SW, ST, and CT. It is necessary to consider the type of movement when comparing the surrogate predictors of knee medial force, as the prediction equations differ significantly among movement types.

Sensor-Based Gait and Balance Assessment in Healthy Adults: Analysis of Short-Term Training and Sensor Placement Effects.

While the analysis of gait and balance can be an important indicator of age- or disease-related changes, it remains unclear if repeated performance of gait and balance tests in healthy adults leads to habituation effects, if short-term gait and balance training can improve gait and balance performance, and whether the placement of wearable sensors influences the measurement accuracy. Healthy adults were assessed before and after performing weekly gait and balance tests over three weeks by using a force plate, motion capturing system and smartphone. The intervention group (n = 25) additionally received a home-based gait and balance training plan. Another sample of healthy adults (n = 32) was assessed once to analyze the impact of sensor placement (lower back vs. lower abdomen) on gait and balance analysis. Both the control and intervention group exhibited improvements in gait/stance. However, the trends over time were similar for both groups, suggesting that targeted training and repeated task performance equally contributed to the improvement of the measured variables. Since no significant differences were found in sensor placement, we suggest that a smartphone used as a wearable sensor could be worn both on the lower abdomen and the lower back in gait and balance analyses.

Evaluating the Performance of Joint Angle Estimation Algorithms on an Exoskeleton Mock-Up via a Modular Testing Approach.

A common challenge for exoskeleton control is discerning operator intent to provide seamless actuation of the device with the operator. One way to accomplish this is with joint angle estimation algorithms and multiple sensors on the human-machine system. However, the question remains of what can be accomplished with just one sensor. The objective of this study was to deploy a modular testing approach to test the performance of two joint angle estimation models-a kinematic extrapolation algorithm and a Random Forest machine learning algorithm-when each was informed solely with kinematic gait data from a single potentiometer on an ankle exoskeleton mock-up. This study demonstrates (i) the feasibility of implementing a modular approach to exoskeleton mock-up evaluation to promote continuity between testing configurations and (ii) that a Random Forest algorithm yielded lower realized errors of estimated joint angles and a decreased actuation time than the kinematic model when deployed on the physical device.