Validity and reliability of a portable gait analysis system for measuring spatiotemporal gait characteristics: comparison to an instrumented treadmill

Unsupervised learning for real-time and continuous gait phase detection

Individuals with lower limb impairment after a stroke or spinal cord injury require rehabilitation, but traditional methods can be challenging for both patients and therapists. Robotic systems have been developed to help; however, they currently cannot detect the continuous gait phase in real time, hindering their effectiveness. To address this limitation, researchers have attempted to develop gait phase detection in general using fuzzy logic algorithms and neural networks. However, there is a paucity of research on real-time and continuous gait phase detection. In light of this gap, we propose an unsupervised learning method for real-time and continuous gait phase detection. This method employs windows of real-time trajectories and a pre-trained model, utilizing trajectories from treadmill walking data, to detect the real-time and continuous gait phase of human on overground locomotion. The neural network model that we have developed exhibits an average time error of less than 11.51 ms across all walking conditions, indicating its suitability for real-time applications. Specifically, the average time error during overground walking at different speeds is 11.20 ms, which is comparatively lower than the average time error observed during treadmill walking, where it is 12.42 ms. By utilizing this method, we can predict the real-time phase using a pre-trained model from treadmill walking data collected with a full motion capture system, which can be performed in a laboratory setting, thereby eliminating the need for overground walking data, which can be more challenging to obtain due to the complexity of the setting.

Validity and reliability of running gait measurement with the ViMove2 system

Running biomechanics have traditionally been analysed in laboratory settings, but this may not reflect natural running gait. Wearable technology has the potential to enable precise monitoring of running gait beyond the laboratory. This study aimed to evaluate the analytical validity and intra-session reliability of temporal running gait outcomes measured by the ViMove2 wearable system in healthy adults. Seventy-four healthy adults (43 males, 31 females, aged 18-55 years) wore the inertial device, ViMove2 on the tibia. Participants ran on a treadmill for one minute at various speeds (8, 10, 12, 14km/hr), completed in a standardised shoe (Saucony Guide Runner). Running gait was measured with the ViMove2 wearable and 3D motion capture (Vicon). Temporal running gait outcomes included ground contact time (GCT) and cadence (steps/min). GCT and cadence from the ViMove2 had face validity with expected changes in outcome with different running speeds, but ViMove2 tended to over-estimate GCT, and under-estimate cadence compared to the reference, especially at slower speeds. GCT demonstrated moderate to good agreement to the reference at speeds >10km/hr, but poor agreement at 8km/hr and within female runners. Cadence had moderate to excellent agreement across speeds compared to the reference. GCT and cadence had excellent reliability across speeds, but at 8km/hr GCT had good agreement between trials. Overall, temporal gait outcomes of GCT and cadence can be measured with the ViMove2, but accuracy and reliability are impacted at slow running speeds and within female runners. Future work is needed to clarify sex or speed-dependent corrections to algorithms / outcomes to aid interpretation and application.

Spatiotemporal Gait Analysis of Patients with Spinocerebellar Ataxia Types 3 and 10 Using Inertial Measurement Units: A Comparative Study

Given the high morbidity related to the progression of gait deficits in spinocerebellar ataxias (SCA), there is a growing interest in identifying biomarkers that can guide early diagnosis and rehabilitation. Spatiotemporal parameter (STP) gait analysis using inertial measurement units (IMUs) has been increasingly studied in this context. This study evaluated STP profiles in SCA types 3 and 10, compared them to controls, and correlated them with clinical scales. IMU portable sensors were used to measure STPs under four gait conditions: self-selected pace (SSP), fast pace (FP), fast pace checking-boxes (FPCB), and fast pace with serial seven subtractions (FPS7). Compared to healthy subjects, both SCA groups had higher values for step time, variability, and swing time, with lower values for gait speed, cadence, and step length. We also found a reduction in speed gain capacity in both SCA groups compared to controls and an increase in speed dual-task cost in the SCA10 group. However, there were no significant differences between the SCA groups. Swing time, mean speed, and step length were correlated with disease severity, risk of falling and functionality in both clinical groups. In the SCA3 group, fear of falling was correlated with cadence. In the SCA10 group, results of the Montreal cognitive assessment test were correlated with step time, mean speed, and step length. These results show that individuals with SCA3 and SCA10 present a highly variable, short-stepped, slow gait pattern compared to healthy subjects, and their gait quality worsened with a fast pace and dual-task involvement.

Clinical and device-based predictors of improved experience of activities of daily living after a multidisciplinary inpatient treatment for people with Parkinson's disease: a cohort study

Background

The inpatient Parkinson's Disease Multimodal Complex Treatment (PD-MCT) is an important therapeutical approach to improving gait and activities of daily living (ADL) of people with PD (PwP). Wearable device-based parameters (DBP) are new options for specific gait analyses toward individualized treatments.

Objectives

We sought to identify predictors of perceived ADL benefit taking clinical scores and DBP into account. Additionally, we analyzed DBP and clinical scores before and after PD-MCT.

Design

Exploratory observational cohort study.

Methods

Clinical scores and DBP of 56 PwP (mean age: 66.3 years, median Hoehn and Yahr (H&Y) stage: 2.5) were examined at the start and the end of a 14-day inpatient PD-MCT in a German University Medical Center. Participants performed four straight walking tasks under single- and dual-task conditions for gait analyses. Additionally, clinical scores of motor and nonmotor functions and quality of life (QoL) were assessed. Using dichotomized data of change in Movement Disorders Society Unified Parkinson's Disease Rating Scale Part II (MDS-UPDRS II) as a dependent variable and clinical and DBP as independent variables, a binomial logistic regression model was implemented.

Results

Young age, high perceived ADL impairment at baseline, high dexterity skills, and a steady gait were significant predictors of ADL benefit after PD-MCT. DBP like gait speed, number of steps, step time, stance time, and double limb support time were improved after PD-MCT. In addition, motor functions (e.g., MDS-UPDRS III and IV), QoL, perceived ADL (MDS-UPDRS II), and experience of nonmotor functions (MDS-UPDRS I) improved significantly.

Conclusion

The logistic regression model identified a group of PwP who had the most probable perceived ADL benefit after PD-MCT. Additionally, gait improved toward a faster and more dynamic gait. Using wearable technology in context of PD-MCT is promising to offer more personalized therapeutical concepts.

Trial registration

German Clinical Trial Register, https://drks.de; DRKS00020948 number, 30 March 2020, retrospectively registered.

Accuracy of unilateral and bilateral gait assessment using a mobile gait analysis system at different walking speeds

BACKGROUND

Previous research on the accuracy of mobile measurement systems has focused on parameters related to the whole gait cycle. Specifically, bilateral gait characteristics were primarily used as outcome measures.

RESEARCH QUESTION

How accurate are unilateral gait characteristics detected using a mobile system at various fixed walking speeds?

METHODS

Gait analysis during treadmill walking at velocities (VEL) of 2.5 (v1), 4.5 (v2) and 6.5 km/h (v3) was performed in a population of 47 healthy young adults, consisting of 27 females (age: 23 ± 2 years, BMI: 21.4 ± 2.2 kg/m²) and 20 males (age: 22 ± 1 years, BMI: 23.3 ± 3.4 kg/m²). Spatiotemporal gait data were simultaneously determined using an instrumented treadmill (gaitway 3D) and a mobile gait analysis system (RehaGait). Besides VEL, bilateral (stride length [SL], cadence [CAD]) and unilateral (contact duration [CON], single [SS] and double support duration [DS]) outcomes were validated.

RESULTS

Across the three VEL investigated, the correlations between both measurement systems were almost perfect in SL and CAD (r > 0.97). In addition, SL significantly differed (p < 0.01) with moderate to large effects, whereby the root mean squared error (RMSE) did not exceed 1.8 cm. RMSE in CAD was not higher than 0.33 spm and statistically significant differences were only present at v1 (d = 0.63). DS was the most erroneous unilateral parameter with values for %RMSE ranging from 9% at v1 to 14% at v3. In CON and SS %RMSE was in a magnitude of 2-4% across all VEL. Furthermore, VEL affected measurement accuracy in unilateral outcomes with moderate to large effects (F (2, 45) > 6.0, p < 0.01, ηp2 > 0.11) with consistently higher differences at lower velocities.

SIGNIFICANCE

Based on the results presented the validity of the mobile gait analysis system investigated to detect gait asymmetries must be questioned.

Classification of inertial sensor-based gait patterns of orthopaedic conditions using machine learning: A pilot study

Elderly patients often have more than one disease that affects walking behavior. An objective tool to identify which disease is the main cause of functional limitations may aid clinical decision making. Therefore, we investigated whether gait patterns could be used to identify degenerative diseases using machine learning. Data were extracted from a clinical database that included sagittal joint angles and spatiotemporal parameters measured using seven inertial sensors, and anthropometric data of patients with unilateral knee or hip osteoarthritis, lumbar or cervical spinal stenosis, and healthy controls. Various classification models were explored using the MATLAB Classification Learner app, and the optimizable Support Vector Machine was chosen as the best performing model. The accuracy of discrimination between healthy and pathologic gait was 82.3%, indicating that it is possible to distinguish pathological from healthy gait. The accuracy of discrimination between the different degenerative diseases was 51.4%, indicating the similarities in gait patterns between diseases need to be further explored. Overall, the differences between pathologic and healthy gait are distinct enough to classify using a classical machine learning model; however, routinely recorded gait characteristics and anthropometric data are not sufficient for successful discrimination of the degenerative diseases.

Validity and reliability of the DANU sports system for walking and running gait assessment

Objective. Gait assessments have traditionally been analysed in laboratory settings, but this may not reflect natural gait. Wearable technology may offer an alternative due to its versatility. The purpose of the study was to establish the validity and reliability of temporal gait outcomes calculated by the DANU sports system, against a 3D motion capture reference system.Approach. Forty-one healthy adults (26 M, 15 F, age 36.4 ± 11.8 years) completed a series of overground walking and jogging trials and 60 s treadmill walking and running trials at various speeds (8-14 km hr-1), participants returned for a second testing session to repeat the same testing.Main results. For validity, 1406 steps and 613 trials during overground and across all treadmill trials were analysed respectively. Temporal outcomes generated by the DANU sports system included ground contact time, swing time and stride time all demonstrated excellent agreement compared to the laboratory reference (intraclass correlation coefficient (ICC) > 0.900), aside from ground contact time during overground jogging which had good agreement (ICC = 0.778). For reliability, 666 overground and 511 treadmill trials across all speeds were examined. Test re-test agreement was excellent for all outcomes across treadmill trials (ICC > 0.900), except for swing time during treadmill walking which had good agreement (ICC = 0.886). Overground trials demonstrated moderate to good test re-test agreement (ICC = 0.672-0.750), which may be due to inherent variability of self-selected (rather than treadmill set) pacing between sessions.Significance. Overall, this study showed that temporal gait outcomes from the DANU Sports System had good to excellent validity and moderate to excellent reliability in healthy adults compared to an established laboratory reference.

Quantitative assessment of the gait improvement effect of LSVT BIG® using a wearable sensor in patients with Parkinson's disease

Background

The main effects of Lee Silvermann Voice Treatment-BIG® therapy (LSVT-BIG) on gait function are improvements in gait speed and stride length. Considering the mechanism of this improvement, LSVT-BIG may affect joint angles of the lower extremities. Therefore, further investigation of the effect of LSVT-BIG on gait function, especially joint angles, is needed.

Methods

Patients with Parkinson's disease (PD) who were eligible for LSVT-BIG were recruited. We measured the following items pre- and post-LSVT-BIG: MDS-Unified Parkinson's Disease Rating Scale (MDS-UPDRS), Functional Independence Measure (FIM), timed up and go test (TUG), and gait parameters using RehaGait®. Gait parameters included gait speed, stride duration and length, the standard deviation of stride duration and length, cadence, the ratio of the stance/swing phase, and the flexion and extension angles of the hip, knee, and ankle joints. Range of motion (ROM) was calculated as the difference of values between the maximum flexion and extension angles of each joint.

Results

Twenty-four participants completed the LSVT-BIG. Significant improvement was observed in the MDS-UPDRS (mean changes: Part I, -2.4 points; Part II, -3.5 points; Part III -8.9 points), TUG (-0.61 s), gait speed (+0.13 m/s), stride length (+0.12 m), flexion and extension angles and ROM of the hip joints (flexion, +2.0°; extension, +2.0; ROM, +4.0°). Enlargement in ROM of the hip joint was strongly correlated with increase in gait speed and stride length (r = 0.755, r = 0.804, respectively).

Conclusions

LSVT-BIG enlarged flexion and extension angles and ROM of the hip joint significantly. Change of ROM of the hip joint was directly related to the increase in stride length and gait speed observed in patients with PD after LSVT-BIG.

Effects of Trail Running versus Road Running-Effects on Neuromuscular and Endurance Performance-A Two Arm Randomized Controlled Study

Running on less predictable terrain has the potential to increase the stimulation of the neuromuscular system and can boost aerobic performance. Hence, the purpose of this study was to analyze the effects of trail versus road running on neuromuscular and endurance performance parameters in running novices. Twenty sedentary participants were randomly assigned to either a trail (TRAIL; n = 10) or road running (ROAD; n = 10) group. A supervised and progressive, moderate intensity, and work-load-matched 8 wk endurance running program on TRAIL or ROAD was prescribed (i.e., randomized). Static balance (BESS test), dynamic balance (Y-balance test), gait analysis (RehaGait test, with regard to stride time single task, stride length dual task, velocity single task), agility performance (t-test), isokinetic leg strength (BIODEX), and predicted VO2max were assessed in pre- and post-tests. rANOVA analysis revealed no significant time-group interactions. Large effect sizes (Cohen's d) for pairwise comparison were found for TRAIL in the BESS test (d = 1.2) and predicted (pred) VO2max (d = 0.95). Moderate effects were evident for ROAD in BESS (d = 0.5), stride time single task (d = 0.52), and VO2max predicted (d = 0.53). Possible moderate to large effect sizes for stride length dual task (72%), velocity single task (64%), BESS test (60%), and the Y-balance test left stance (51%) in favor of TRAIL occurred. Collectively, the results suggested slightly more beneficial tendencies in favor of TRAIL. Additional research is needed to clearly elucidate differences between TRAIL and ROAD, not only in novices but also in experienced exercisers.

Validity of temporo-spatial characteristics of gait as an index for fall risk screening in community-dwelling older people

[Purpose] This study aimed to identify kinematic gait indicators for a fall risk screening test through quantitative comparisons of gait characteristics measured using mobile inertial sensors between faller and non-faller groups in a population of community-dwelling older people. [Participants and Methods] We enrolled 50 people aged ≥65 years who used long-term care prevention services, interviewed them to determine their fall history during the past year, and divided them into faller and non-faller groups. Gait parameters (velocity, cadence, stride length, foot height, heel strike angle, ankle joint angle, knee joint angle, and hip joint angle) were assessed using the mobile inertial sensors. [Results] Gait velocity and left and right heel strike angles were significantly lower and smaller, respectively, in the faller versus non-faller group. Receiver operating characteristic curve analysis revealed areas under the curve of 0.686, 0.722, and 0.691 for gait velocity, left heel strike angle, and right heel strike angle, respectively. [Conclusion] Gait velocity and heel strike angle during gait assessed using mobile inertial sensors may be important kinematic indicators in a fall risk screening test to estimate the likelihood of falls among community-dwelling older people.

Characterization of the Kinetyx SI Wireless Pressure-Measuring Insole during Benchtop Testing and Running Gait

This study characterized the absolute pressure measurement error and reliability of a new fully integrated (Kinetyx, SI) plantar-pressure measurement system (PPMS) versus an industry-standard PPMS (F-Scan, Tekscan) during an established benchtop testing protocol as well as via a research-grade, instrumented treadmill (Bertec) during a running protocol. Benchtop testing results showed that both SI and F-Scan had strong positive linearity (Pearson's correlation coefficient, PCC = 0.86-0.97, PCC = 0.87-0.92; RMSE = 15.96 ± 9.49) and mean root mean squared error RMSE (9.17 ± 2.02) compared to the F-Scan on a progressive loading step test. The SI and F-Scan had comparable results for linearity and hysteresis on a sinusoidal loading test (PCC = 0.92-0.99; 5.04 ± 1.41; PCC = 0.94-0.99; 6.15 ± 1.39, respectively). SI had less mean RMSE (6.19 ± 1.38) than the F-Scan (8.66 ±2.31) on the sinusoidal test and less absolute error (4.08 ± 3.26) than the F-Scan (16.38 ± 12.43) on a static test. Both the SI and F-Scan had near-perfect between-day reliability interclass correlation coefficient, ICC = 0.97-1.00) to the F-Scan (ICC = 0.96-1.00). During running, the SI pressure output had a near-perfect linearity and low RMSE compared to the force measurement from the Bertec treadmill. However, the SI pressure output had a mean hysteresis of 7.67% with a 28.47% maximum hysteresis, which may have implications for the accurate quantification of kinetic gait measures during running.

Gait Characteristics Associated with Fear of Falling in Hospitalized People with Parkinson's Disease

BACKGROUND

Fear of falling (FOF) is common in Parkinson's disease (PD) and associated with distinct gait changes. Here, we aimed to answer, how quantitative gait assessment can improve our understanding of FOF-related gait in hospitalized geriatric patients with PD.

METHODS

In this cross-sectional study of 79 patients with advanced PD, FOF was assessed with the Falls Efficacy Scale International (FES-I), and spatiotemporal gait parameters were recorded with a mobile gait analysis system with inertial measurement units at each foot while normal walking. In addition, demographic parameters, disease-specific motor (MDS-revised version of the Unified Parkinson's Disease Rating Scale, Hoehn & Yahr), and non-motor (Non-motor Symptoms Questionnaire, Montreal Cognitive Assessment) scores were assessed.

RESULTS

According to the FES-I, 22.5% reported low, 28.7% moderate, and 47.5% high concerns about falling. Most concerns were reported when walking on a slippery surface, on an uneven surface, or up or down a slope. In the final regression model, previous falls, more depressive symptoms, use of walking aids, presence of freezing of gait, and lower walking speed explained 42% of the FES-I variance.

CONCLUSION

Our study suggests that FOF is closely related to gait changes in hospitalized PD patients. Therefore, FOF needs special attention in the rehabilitation of these patients, and targeting distinct gait parameters under varying walking conditions might be a promising part of a multimodal treatment program in PD patients with FOF. The effect of these targeted interventions should be investigated in future trials.

Biomechanics beyond the lab: Remote technology for osteoarthritis patient data-A scoping review

The objective of this project is to produce a review of available and validated technologies suitable for gathering biomechanical and functional research data in patients with osteoarthritis (OA), outside of a traditionally fixed laboratory setting. A scoping review was conducted using defined search terms across three databases (Scopus, Ovid MEDLINE, and PEDro), and additional sources of information from grey literature were added. One author carried out an initial title and abstract review, and two authors independently completed full-text screenings. Out of the total 5,164 articles screened, 75 were included based on inclusion criteria covering a range of technologies in articles published from 2015. These were subsequently categorised by technology type, parameters measured, level of remoteness, and a separate table of commercially available systems. The results concluded that from the growing number of available and emerging technologies, there is a well-established range in use and further in development. Of particular note are the wide-ranging available inertial measurement unit systems and the breadth of technology available to record basic gait spatiotemporal measures with highly beneficial and informative functional outputs. With the majority of technologies categorised as suitable for part-remote use, the number of technologies that are usable and fully remote is rare and they usually employ smartphone software to enable this. With many systems being developed for camera-based technology, such technology is likely to increase in usability and availability as computational models are being developed with increased sensitivities to recognise patterns of movement, enabling data collection in the wider environment and reducing costs and creating a better understanding of OA patient biomechanical and functional movement data.

mVEGAS - mobile smartphone-based spatiotemporal gait analysis in healthy and ataxic gait disorders

BACKGROUND

Quantitative gait assessment is increasingly applied in fall risk stratification, diagnosis, and disease monitoring of neuro-geriatric gait disorders. Its broad application, however, demands for low-cost and mobile solutions that facilitate high-quality assessment outside laboratory settings. The aim of this study was to present and evaluate the concurrent validity of a mobile and low-cost gait assessment tool (mVEGAS) that combines body-fixed inertial sensors and a smartphone-based video capture for spatiotemporal identification of gait sequences.

METHODS

Initially, we examined potential interferences of wearing mVEGAS with walking performance in a cohort of 20 young healthy individuals (31.1 ± 10.1 years; 8 females). Subsequently, we assessed the concurrent validity of mVEGAS as compared to a pressure-sensitive gait carpet (GAITRite) in a cohort of 26 healthy individuals (55.8 ± 14.3 years; 10 females) and 26 patients (55.7 ± 14.0; 14 females) with moderate to severe degrees of cerebellar gait ataxia. All participants were instructed to walk at preferred, slow, and fast walking speed and standard average and variability gait measures including velocity, stride length, stride time, base of support, swing and double support phase were examined for agreement between the two systems by absolute error and intraclass correlation coefficients (ICC).

RESULTS

Wearing mVEGAS did only marginally interfere with normal walking behavior. mVEGAS-derived average and variability gait measures exhibited good to excellent concurrent validity in healthy individuals (ICCs ranging between 0.645 and 1.000) and patients with gait ataxia (ICCs ranging between 0.788 and 1.000) SIGNIFICANCE: mVEGAS may facilitate high-quality and long-term gait monitoring in different non-specialized environments such as medical practices, nursing homes or community centers.

A Comparison of Force-Plate Based Center of Mass Estimation Algorithms

Estimating horizontal center of mass (CoM) is an important process that is used in the control of self-paced treadmills, as well as in clinical and scientific biomechanical analysis. Many laboratories use motion-capture to estimate CoM, while others use force-plate based estimates, either because they cannot access motion-capture or they do not want to be taxed with post-processing optoelectronic data. Three force-plate derived center of mass estimation algorithms were compared against a benchmark motion-capture technique. Two of them have recently been reported in the literature, and both rely on numerical integration of 2nd-order differential equations. We propose a third technique that uses an algebraic equation to directly relate center of pressure to center of mass without numerical drift. Twenty-four healthy adults participated in a five-minute steady-state walking test to compare these algorithms. The sample-by-sample standard deviation of the three force-plate based algorithms from the motion-capture benchmark algorithm was evaluated. The algebraic technique provided less error than either of the two more common integration techniques (p<0.05). The results of this study support the viability of using only ground reaction forces for self-paced treadmills and also show that a simple algebraic model is preferred to integration approaches. The use of an algebraic estimation simplifies control implementation for self-paced treadmill applications and eliminates the need for event-based drift recalibration.

The Associations between Plantar Force Distribution and Successfulness in Short-Fire Shooting among Special Police Officers

The main purpose of the study was to determine whether a pistol shooting efficiency score could be predicted by plantar force distribution patterns. In this cross-sectional study, participants were special police male officers (N = 30), members of the Anti-Terrorist Unit ‘Lučko’ (agemean±SD = 40 ± 6 years, heightmean±SD = 180 ± 5 cm, weightmean±SD = 89 ± 8 kg). Shooting efficiency at a target 10 m away was tested on a scale from 0 to 5, while standing on a Zebris pedobarographic platform. Higher absolute (N; β = −0.19, p = 0.002) and relative (%; β = −0.12, p = 0.043) forces beneath the hindfoot were associated with poorer shooting efficiency. A significant positive association between the relative force beneath the forefoot and shooting efficiency was found, i.e., higher relative forces beneath the forefoot region exhibited better shooting values (β = 0.12, p = 0.043). When the force was normalized by weight (N/kg), similar associations remained. This study shows that higher force values under the hindfoot region may lead to a lower shooting performance, while higher force values under the forefoot region can increase shooting performance.

Effect of Unilateral Shoulder Disorder on the Stance Phase of Human Gait

Background. Gait analysis systems serve as important tools for assessing disturbed gait patterns. Amongst other factors, functional limitations of the shoulder joint may relate to such disturbances. Patient-reported outcome measures, assessment of pain, and active range of motion are commonly used to describe shoulder impairment. Purpose. The aim of this cohort study was to evaluate the impact of unilateral limitations of shoulder mobility and pain on gait patterns and to detect correlations between pain, shoulder mobility, and particular phases of human gait using a Zebris gait analysis system. Methods. 20 subjects with unilaterally restricted mobility and pain of the affected shoulder and a control group of 10 healthy subjects underwent a gait analysis. Various gait parameters, the DASH score, pain at rest and movement of the affected shoulder, and the active range of motion (aROM) for shoulder flexion and abduction were recorded. Results. We determined significant differences of the duration of the loading response ( $p$  = 0.021), midstance ( $p$  = 0.033), and the terminal stance phase ( $p$  = 0.019) between the shoulder group and the control group, with a shorter loading response phase and a longer terminal stance phase of the affected side in the shoulder group. In the shoulder group, we found significant correlations between the DASH and the duration of the midstance phase ( $p$  = 0.023) and the terminal stance phase ( $p$  = 0.038). In addition, there was a significant correlation between shoulder flexion and the duration of the midstance phase ( $p$  = 0.047).

Fear of Falling Does Not Influence Dual-Task Gait Costs in People with Parkinson's Disease: A Cross-Sectional Study

Cognitive deficits and fear of falling (FOF) can both influence gait patterns in Parkinson’s disease (PD). While cognitive deficits contribute to gait changes under dual-task (DT) conditions, it is unclear if FOF also influences changes to gait while performing a cognitive task. Here, we aimed to explore the association between FOF and DT costs in PD, we additionally describe associations between FOF, cognition, and gait parameters under single-task and DT. In 40 PD patients, motor symptoms (MDS-revised version of the Unified Parkinson’s Disease Rating Scale, Hoehn and Yahr), FOF (Falls Efficacy Scale International), and Montreal Cognitive Assessment (MoCA) were assessed. Spatiotemporal gait parameters were recorded with a validated mobile gait analysis system with inertial measurement units at each foot while patients walked in a 50 m hallway at their preferred speed under single-task and DT conditions. Under single-task conditions, stride length (β = 0.798) and spatial variability (β = 0.202) were associated with FOF (adjusted R2 = 0.19, p < 0.001) while the MoCA was only weakly associated with temporal variability (adjusted R2 = 0.05, p < 0.001). Under DT conditions, speed, stride length, and cadence decreased, while spatial variability, temporal variability, and stride duration increased with the largest effect size for speed. DT costs of stride length (β = 0.42) and age (β = 0.58) explained 18% of the MoCA variance. However, FOF was not associated with the DT costs of gait parameters. Gait difficulties in PD may exacerbate when cognitive tasks are added during walking. However, FOF does not appear to have a relevant effect on dual-task costs of gait.

Optimal Assistance Timing to Induce Voluntary Dorsiflexion Movements: A Preliminary Study in Healthy Participants

Swing-phase dorsiflexion assistance with robotic ankle–foot orthosis could improve toe clearance and limb shortening such that compensatory movements are suppressed. However, facilitating voluntary effort under assistance remains a challenge. In our previous study, we examined assistance effects of swing-phase dorsiflexion with different delay times after toe-off on a dorsiflexion-restricted gait with a high-dorsiflexion assistive system. Results showed that later dorsiflexion assistance could lead to an increase in the tibialis anterior’s surface electromyography but could also deteriorate compensatory movement. Thus, we concluded that there is a suitable assistance timing to simultaneously achieve voluntary effort and optimal gait. In the present research, we derived a method to identify a suitable dorsiflexion assistance delay time via a multiple linear regression analysis on ankle data of stroke patients with a pathological gait with insufficient dorsiflexion. With the identification method, an experiment was conducted on six healthy participants with restricted dorsiflexion. Results showed that the identified assistance timing improved the amplitude of the tibialis anterior’s surface electromyography while also suppressing limb shortening during circumduction and hip hiking. Although a practical study of stroke survivors is required, observations from this research indicate the potential to successfully induce voluntary efforts with the identified dorsiflexion assistance timing.

6MWT on a new self-paced treadmill system compared with overground

The 6-min walk test (6MWT) is a useful tool for clinicians and researchers to estimate gait performance and fatigue affecting functional mobility. A modified 6MWT administered on a treadmill (TM) can be an efficient, space-saving alternative to perform the 6MWT. The aim of this study was to investigate if a 6MWT on a self-paced (SP) TM produced similar results compared to an overground (OG) 6MWT among healthy participants with the hypothesis that users would demonstrate similar gait parameters. The second aim was to assess the reliability of SP TM sessions with the hypothesis that gait parameters would be reliable. Twelve healthy young adults performed one OG 6MWT and two SP TM 6MWTs, with the TM tests performed on two different testing days. The OG 6MWTs were conducted along a 20 m corridor with a portable optometric system. The SP TM 6MWTs were performed using a dual-belt instrumented TM with speed controlled by feedback from a LIDAR sensor. In the OG condition, participants walked 664.8 m ± 48.9 m when the standard method was used to calculate distance and 721.3 m ± 56.2 m with an average-speed-based estimation of distance, which corrects for U-turns. For the SP TM 6MWT, they covered 729.4 m ± 45.8 m in the first session and 727.4 m ± 56.0 m in the second session. Gait parameters showed good to excellent within- and between-day reliability on the adaptive TM. Gait parameters were similar between modalities. A significant difference in the 6MWT distance was found between modalities. This is attributable to the U-turns, because a comparison between TM 6MWT distance and the average-speed-based estimation of the distance for the OG modality showed no significant difference. However, this system produced similar spatiotemporal gait parameters among participants compared to OG.

Peripheral neuropathy in Parkinson's disease: prevalence and functional impact on gait and balance

Peripheral neuropathy is a common problem in patients with Parkinson's disease. Peripheral neuropathy's prevalence in Parkinson's disease varies between 4.8-55%, compared with 9% in the general population. It remains unclear whether peripheral neuropathy leads to decreased motor performance in Parkinson's disease, resulting in impaired mobility and increased balance deficits. We aimed to determine the prevalence and type of peripheral neuropathy in Parkinson's disease patients and evaluate its functional impact on gait and balance. A cohort of consecutive Parkinson's disease patients assessed by movement disorders specialists based on the UK Brain Bank criteria underwent clinical, neurophysiological (nerve conduction studies and quantitative sensory testing) and neuropathological (intraepidermal nerve fibre density in skin biopsy punches) evaluation to characterize the peripheral neuropathy type and aetiology using a cross-sectional design. Gait and balance were characterized using wearable health-technology in OFF and ON medication states, and the main parameters were extracted using validated algorithms. A total of 99 Parkinson's disease participants with a mean age of 67.2 (±10) years and mean disease duration of 6.5 (±5) years were assessed. Based on a comprehensive clinical, neurophysiological and neuropathological evaluation, we found that 40.4% of Parkinson's disease patients presented peripheral neuropathy, with a predominance of small fibre neuropathy (70% of the group). In the OFF state, the presence of peripheral neuropathy was significantly associated with shorter stride length (P = 0.029), slower gait speed (P = 0.005) and smaller toe-off angles (P = 0.002) during straight walking; significantly slower speed (P = 0.019) and smaller toe-off angles (P = 0.007) were also observed during circular walking. In the ON state, the above effects remained, albeit moderately reduced. With regard to balance, significant differences between Parkinson's disease patients with and without peripheral neuropathy were observed in the OFF medication state during stance with closed eyes on a foam surface. In the ON states, these differences were no longer observable. We showed that peripheral neuropathy is common in Parkinson's disease and influences gait and balance parameters, as measured with mobile health-technology. Our study supports that peripheral neuropathy recognition and directed treatment should be pursued in order to improve gait in Parkinson's disease patients and minimize balance-related disability, targeting individualized medical care.

Calibration-Free Gait Assessment by Foot-Worn Inertial Sensors

Walking is a central activity of daily life, and there is an increasing demand for objective measurement-based gait assessment. In contrast to stationary systems, wearable inertial measurement units (IMUs) have the potential to enable non-restrictive and accurate gait assessment in daily life. We propose a set of algorithms that uses the measurements of two foot-worn IMUs to determine major spatiotemporal gait parameters that are essential for clinical gait assessment: durations of five gait phases for each side as well as stride length, walking speed, and cadence. Compared to many existing methods, the proposed algorithms neither require magnetometers nor a precise mounting of the sensor or dedicated calibration movements. They are therefore suitable for unsupervised use by non-experts in indoor as well as outdoor environments. While previously proposed methods are rarely validated in pathological gait, we evaluate the accuracy of the proposed algorithms on a very broad dataset consisting of 215 trials and three different subject groups walking on a treadmill: healthy subjects (n = 39), walking at three different speeds, as well as orthopedic (n = 62) and neurological (n = 36) patients, walking at a self-selected speed. The results show a very strong correlation of all gait parameters (Pearson's r between 0.83 and 0.99, p < 0.01) between the IMU system and the reference system. The mean absolute difference (MAD) is 1.4 % for the gait phase durations, 1.7 cm for the stride length, 0.04 km/h for the walking speed, and 0.7 steps/min for the cadence. We show that the proposed methods achieve high accuracy not only for a large range of walking speeds but also in pathological gait as it occurs in orthopedic and neurological diseases. In contrast to all previous research, we present calibration-free methods for the estimation of gait phases and spatiotemporal parameters and validate them in a large number of patients with different pathologies. The proposed methods lay the foundation for ubiquitous unsupervised gait assessment in daily-life environments.

Development of a vibrotactile cueing device that implicitly increases walking speed during gait training in stroke patients: an observational case series study

One of the main sequelae of stroke is difficulty walking, which is characterised by decreased walking speed and asymmetrical walking patterns. Physical therapists often rely on explicit motor learning strategies, i.e., providing mainly verbal instructions for how movements should be performed. However, the voluntary movement induced by explicit instruction may lead to associated unintended muscle contractions or higher cognitive demand, which could be detrimental. We introduce a vibrotactile cueing device that implicitly improves walking speed. The stroke patient walks while alternating vibrational cues are given to the left and right sides of their waist. At each specified step, cueing frequency increases in the cueing group without the patient's awareness. The four patients in the cueing group did not notice the increase in walking speed during training; however, we observed an improvement in walking speed and cadence in patients using the proposed cueing system, which was maintained during the posttest phase. Additionally, patients using the cueing system were able to suppress excessive compensatory movements during training compared with patients who did not use the system. This case series study indicates that the proposed system for gait rehabilitation of stroke patients can enable an increase in walking speed without excessive effort.

The Accuracy and Precision of Gait Spatio-Temporal Parameters Extracted from an Instrumented Sock during Treadmill and Overground Walking in Healthy Subjects and Patients with a Foot Impairment Secondary to Psoriatic Arthritis

The objectives of this study were to assess the accuracy and precision of a system combining an IMU-instrumented sock and a validated algorithm for the estimation of the spatio-temporal parameters of gait. A total of 25 healthy participants (HP) and 21 patients with foot impairments secondary to psoriatic arthritis (PsA) performed treadmill walking at three different speeds and overground walking at a comfortable speed. HP performed the assessment over two sessions. The proposed system's estimations of cadence (CAD), gait cycle duration (GCD), gait speed (GS), and stride length (SL) obtained for treadmill walking were validated versus those estimated with a motion capture system. The system was also compared with a well-established multi-IMU-based system for treadmill and overground walking. The results showed a good agreement between the motion capture system and the IMU-instrumented sock in estimating the spatio-temporal parameters during the treadmill walking at normal and fast speeds for both HP and PsA participants. The accuracy of GS and SL obtained from the IMU-instrumented sock was better compared to the established multi-IMU-based system in both groups. The precision (inter-session reliability) of the gait parameter estimations obtained from the IMU-instrumented sock was good to excellent for overground walking and treadmill walking at fast speeds, but moderate-to-good for slow and normal treadmill walking. The proposed IMU-instrumented sock offers a novel form factor addressing the wearability issues of IMUs and could potentially be used to measure spatio-temporal parameters under clinical conditions and free-living conditions.

TRIPOD—A Treadmill Walking Dataset with IMU, Pressure-Distribution and Photoelectric Data for Gait Analysis

Inertial measurement units (IMUs) enable easy to operate and low-cost data recording for gait analysis. When combined with treadmill walking, a large number of steps can be collected in a controlled environment without the need of a dedicated gait analysis laboratory. In order to evaluate existing and novel IMU-based gait analysis algorithms for treadmill walking, a reference dataset that includes IMU data as well as reliable ground truth measurements for multiple participants and walking speeds is needed. This article provides a reference dataset consisting of 15 healthy young adults who walked on a treadmill at three different speeds. Data were acquired using seven IMUs placed on the lower body, two different reference systems (Zebris FDMT-HQ and OptoGait), and two RGB cameras. Additionally, in order to validate an existing IMU-based gait analysis algorithm using the dataset, an adaptable modular data analysis pipeline was built. Our results show agreement between the pressure-sensitive Zebris and the photoelectric OptoGait system (r = 0.99), demonstrating the quality of our reference data. As a use case, the performance of an algorithm originally designed for overground walking was tested on treadmill data using the data pipeline. The accuracy of stride length and stride time estimations was comparable to that reported in other studies with overground data, indicating that the algorithm is equally applicable to treadmill data. The Python source code of the data pipeline is publicly available, and the dataset will be provided by the authors upon request, enabling future evaluations of IMU gait analysis algorithms without the need of recording new data.

A Pressure-Pad-Embedded Treadmill Yields Time-Dependent Errors in Estimating Ground Reaction Force during Walking

Accurate and reliable vertical ground reaction force (VGRF) measurement is essential in various biomechanical and clinical studies. Recently, pressure-pad-embedded treadmills have been widely used for VGRF measurement as a relatively less expensive option than the force platform-mounted treadmills. Prior studies have shown that the popular Zebris treadmill is reliable when used to measure peak VGRF for short walking sessions. However, comprehensive evaluation of human walking requires information of gait parameters over sufficient gait cycles. In this study, we quantify the long-term temporal changes in VGRF values measured by the Zebris treadmill. Twenty participants walked on the treadmill for 10 min twice, with 10 min rest between trials. We found an evident decline in the measured VGRF and impulse over time for both trials. The Zebris system also consistently yielded the lower VGRF values during the second trials. These results indicate that the Zebris treadmill is unreliable in measuring VGRF during walking, and a 10 min break is not enough for the embedded sensors to recover their sensitivity. We provided a way to resolve these time-dependent errors; using the impulse-momentum theorem and collected kinematics of the participants, we formulated a curve-fitting model encapsulating the growing VGRF estimation error.

Assessing Site Specificity of Osteoarthritic Gait Kinematics with Wearable Sensors and Their Association with Patient Reported Outcome Measures (PROMs): Knee versus Hip Osteoarthritis

There is a great need for quantitative outcomes reflecting the functional status in patients with knee or hip osteoarthritis (OA) to advance the development and investigation of interventions for OA. The purpose of this study was to determine if gait kinematics specific to the disease—i.e., knee versus hip OA—can be identified using wearable sensors and statistical parametric mapping (SPM) and whether disease-related gait deviations are associated with patient reported outcome measures. 113 participants (N = 29 unilateral knee OA; N = 30 unilateral hip OA; N = 54 age-matched asymptomatic persons) completed gait analysis with wearable sensors and the Knee/Hip Osteoarthritis Outcome Score (KOOS/HOOS). Data were analyzed using SPM. Knee and hip kinematics differed between patients with knee OA and patients with hip OA (up to 14°, p < 0.001 for knee and 8°, p = 0.003 for hip kinematics), and differences from controls were more pronounced in the affected than unaffected leg of patients. The observed deviations in ankle, knee and hip kinematic trajectories from controls were associated with KOOS/HOOS in both groups. Capturing gait kinematics using wearables has a large potential for application as outcome in clinical trials and for monitoring treatment success in patients with knee or hip OA and in large cohorts representing a major advancement in research on musculoskeletal diseases.

Measuring Spatiotemporal Parameters on Treadmill Walking Using Wearable Inertial System

This study aims to measure and compare spatiotemporal gait parameters in nineteen subjects using a full wearable inertial mocap system Xsens (MVN Awinda, Netherlands) and a photoelectronic system one-meter OptoGaitTM (Microgait, Italy) on a treadmill imposing a walking speed of 5 km/h. A total of eleven steps were considered for each subject constituting a dataset of 209 samples from which spatiotemporal parameters (SPT) were calculated. The step length measurement was determined using two methods. The first one considers the calculation of step length based on the inverted pendulum model, while the second considers an anthropometric approach that correlates the stature with an anthropometric coefficient. Although the absolute agreement and consistency were found for the calculation of the stance phase, cadence and gait cycle, from our study, differences in SPT were found between the two systems. Mean square error (MSE) calculation of their speed (m/s) with respect to the imposed speed on a treadmill reveals a smaller error (MSE = 0.0008) using the OptoGaitTM. Overall, our results indicate that the accurate detection of heel strike and toe-off have an influence on phases and sub-phases for the entire acquisition. Future study in this domain should investigate how to design and integrate better products and algorithms aiming to solve the problematic issues already identified in this study without limiting the user's need and performance in a different environment.

Validity of Inertial Sensors for Assessing Balance Kinematics and Mobility during Treadmill-Based Perturbation and Dance Training

Inertial sensors (IS) enable the kinematic analysis of human motion with fewer logistical limitations than the silver standard optoelectronic motion capture (MOCAP) system. However, there are no data on the validity of IS for perturbation training and during the performance of dance. The aim of this present study was to determine the concurrent validity of IS in the analysis of kinematic data during slip and trip-like perturbations and during the performance of dance. Seven IS and the MOCAP system were simultaneously used to capture the reactive response and dance movements of fifteen healthy young participants (Age: 18-35 years). Bland Altman (BA) plots, root mean square errors (RMSE), Pearson's correlation coefficients (R), and intraclass correlation coefficients (ICC) were used to compare kinematic variables of interest between the two systems for absolute equivalency and accuracy. Limits of agreements (LOA) of the BA plots ranged from -0.23 to 0.56 and -0.21 to 0.43 for slip and trip stability variables, respectively. The RMSE for slip and trip stabilities were from 0.11 to 0.20 and 0.11 to 0.16, respectively. For the joint mobility in dance, LOA varied from -6.98-18.54, while RMSE ranged from 1.90 to 13.06. Comparison of IS and optoelectronic MOCAP system for reactive balance and body segmental kinematics revealed that R varied from 0.59 to 0.81 and from 0.47 to 0.85 while ICC was from 0.50 to 0.72 and 0.45 to 0.84 respectively for slip-trip perturbations and dance. Results of moderate to high concurrent validity of IS and MOCAP systems. These results were consistent with results from similar studies. This suggests that IS are valid tools to quantitatively analyze reactive balance and mobility kinematics during slip-trip perturbation and the performance of dance at any location outside, including the laboratory, clinical and home settings.

Accuracy and Repeatability of Spatiotemporal Gait Parameters Measured with an Inertial Measurement Unit

In recent years, interest in finding alternatives for the evaluation of mobility has increased. Inertial measurement units (IMUs) stand out for their portability, size, and low price. The objective of this study was to examine the accuracy and repeatability of a commercially available IMU under controlled conditions in healthy subjects. A total of 36 subjects, including 17 males and 19 females were analyzed with a Wiva Science IMU in a corridor test while walking for 10 m and in a threadmill at 1.6 km/h, 2.4 km/h, 3.2 km/h, 4 km/h, and 4.8 km/h for one minute. We found no difference when we compared the variables at 4 km/h and 4.8 km/h. However, we found greater differences and errors at 1.6 km/h, 2.4 km/h and 3.2 km/h, and the latter one (1.6 km/h) generated more error. The main conclusion is that the Wiva Science IMU is reliable at high speeds but loses reliability at low speeds.

Step Length Is a Promising Progression Marker in Parkinson's Disease

Current research on Parkinson’s disease (PD) is increasingly concerned with the identification of objective and specific markers to make reliable statements about the effect of therapy and disease progression. Parameters from inertial measurement units (IMUs) are objective and accurate, and thus an interesting option to be included in the regular assessment of these patients. In this study, 68 patients with PD (PwP) in Hoehn and Yahr (H&Y) stages 1–4 were assessed with two gait tasks—20 m straight walk and circular walk—using IMUs. In an ANCOVA model, we found a significant and large effect of the H&Y scores on step length in both tasks, and only a minor effect on step time. This study provides evidence that from the two potentially most important gait parameters currently accessible with wearable technology under supervised assessment strategies, step length changes substantially over the course of PD, while step time shows surprisingly little change in the progression of PD. These results show the importance of carefully evaluating quantitative gait parameters to make assumptions about disease progression, and the potential of the granular evaluation of symptoms such as gait deficits when monitoring chronic progressive diseases such as PD.

Validity and Reliability of an Instrumented Treadmill with an Accelerometry System for Assessment of Spatio-Temporal Parameters and Impact Transmission

Running retraining programs focused on concurrent feedback of acceleration impacts have been demonstrated to be a good strategy to reduce running-related injuries (RRI), as well as to improve running economy and reduce acceleration impacts and injury running incidence. Traditionally, impacts have been registered by mean of accelerometers attached directly to the athletes, which is inaccessible to the entire population, because it requires laboratory conditions. This study investigated the validity and reliability of a new device integrated directly into the treadmill, compared to a traditional acceleration impact system. Thirty healthy athletes with no history of RRI were tested on two separate days over the instrumented treadmill (AccTrea) and simultaneously with an acceleration impact system attached to the participant (AccAthl). AccTrea was demonstrated to be a valid and reliable tool for measuring spatio-temporal parameters like step length (validity intraclass correlation coefficient (ICC) = 0.94; reliability ICC = 0.92), step time (validity ICC = 0.95; reliability ICC = 0.96), and step frequency (validity ICC = 0.95; reliability ICC = 0.96) during running. Peak acceleration impact variables showed a high reliability for the left (reliability ICC = 0.88) and right leg (reliability ICC = 0.85), and peak impact asymmetry showed a modest validity (ICC = 0.55). These results indicated that the AccTrea system is a valid and reliable way to assess spatio-temporal variables, and a reliable tool for measuring acceleration impacts during running.

Gait Analysis with Wearables Can Accurately Classify Fallers from Non-Fallers: A Step toward Better Management of Neurological Disorders

Falls are the leading cause of mortality, morbidity and poor quality of life in older adults with or without neurological conditions. Applying machine learning (ML) models to gait analysis outcomes offers the opportunity to identify individuals at risk of future falls. The aim of this study was to determine the effect of different data pre-processing methods on the performance of ML models to classify neurological patients who have fallen from those who have not for future fall risk assessment. Gait was assessed using wearables in clinic while walking 20 m at a self-selected comfortable pace in 349 (159 fallers, 190 non-fallers) neurological patients. Six different ML models were trained on data pre-processed with three techniques such as standardisation, principal component analysis (PCA) and path signature method. Fallers walked more slowly, with shorter strides and longer stride duration compared to non-fallers. Overall, model accuracy ranged between 48% and 98% with 43-99% sensitivity and 48-98% specificity. A random forest (RF) classifier trained on data pre-processed with the path signature method gave optimal classification accuracy of 98% with 99% sensitivity and 98% specificity. Data pre-processing directly influences the accuracy of ML models for the accurate classification of fallers. Using gait analysis with trained ML models can act as a tool for the proactive assessment of fall risk and support clinical decision-making.

Evaluation of the repeatability of kinetic and temporospatial gait variables measured with a pressure-sensitive treadmill for dogs

OBJECTIVE

To evaluate intrasession and intersession repeatability of measurements for temporospatial and kinetic variables obtained with a pressure-sensitive treadmill designed for gait analysis of dogs.

ANIMALS

16 client-owned dogs.

PROCEDURES

The influence of treadmill speed on accuracy of ground reaction force (GRF) measurements was assessed by simulated gait analysis at 0 to 7.5 km/h with a custom test device. A similar test was performed with 1 client-owned dog ambulating on the treadmill at 5 speeds (3 to 7 km/h) for GRF calculations. Fifteen client-owned dogs were then walked on the treadmill at 3 km/h for collection of temporospatial and kinetic data. Intrasession repeatability was determined by comparing 2 sets of measurements obtained ≤ 2 hours apart. Intersession repeatability was determined by comparing the first set of these measurements with those for a second session ≥ 4 days later. Intraclass correlation coefficients (ICCs; consistency test) and difference ratios were calculated to assess repeatability.

RESULTS

Increases in treadmill speed yielded a mean 9.1% decrease in weight-normalized force data at belt speeds of up to 7.5 km/h for the test device, compared with the value when the treadmill belt was stationary. Results were similar for the dog at increasing treadmill speeds (mean decrease, 12.4%). For temporospatial data, intrasession ICCs were > 0.9 and intersession ICCs ranged from 0.75 to 0.9; for GRFs, intrasession and intersession ICCs ranged from 0.68 to 0.97 and from 0.35 to 0.78, respectively.

CONCLUSIONS AND CLINICAL RELEVANCE

Repeatability of temporospatial data for healthy dogs was good to excellent; results for kinetic data varied. Further research is needed to investigate use of this system for gait analysis with larger samples of dogs and dogs with lameness.

Testing the Performance of an Innovative Markerless Technique for Quantitative and Qualitative Gait Analysis

Gait abnormalities such as high stride and step frequency/cadence (SF-stride/second, CAD-step/second), stride variability (SV) and low harmony may increase the risk of injuries and be a sentinel of medical conditions. This research aims to present a new markerless video-based technology for quantitative and qualitative gait analysis. 86 healthy individuals (mead age 32 years) performed a 90 s test on treadmill at self-selected walking speed. We measured SF and CAD by a photoelectric sensors system; then, we calculated average ± standard deviation (SD) and within-subject coefficient of variation (CV) of SF as an index of SV. We also recorded a 60 fps video of the patient. With a custom-designed web-based video analysis software, we performed a spectral analysis of the brightness over time for each pixel of the image, that reinstituted the frequency contents of the videos. The two main frequency contents (F1 and F2) from this analysis should reflect the forcing/dominant variables, i.e., SF and CAD. Then, a harmony index (HI) was calculated, that should reflect the proportion of the pixels of the image that move consistently with F1 or its supraharmonics. The higher the HI value, the less variable the gait. The correspondence SF-F1 and CAD-F2 was evaluated with both paired t-Test and correlation and the relationship between SV and HI with correlation. SF and CAD were not significantly different from and highly correlated with F1 (0.893 ± 0.080 Hz vs. 0.895 ± 0.084 Hz, p < 0.001, r² = 0.99) and F2 (1.787 ± 0.163 Hz vs. 1.791 ± 0.165 Hz, p < 0.001, r² = 0.97). The SV was 1.84% ± 0.66% and it was significantly and moderately correlated with HI (0.082 ± 0.028, p < 0.001, r² = 0.13). The innovative video-based technique of global, markerless gait analysis proposed in our study accurately identifies the main frequency contents and the variability of gait in healthy individuals, thus providing a time-efficient, low-cost means to quantitatively and qualitatively study human locomotion.

Comparison of Subjective and Objective Assessments on Improvement in Gait Function after Carotid Endarterectomy

The purpose of the present study was to determine whether objective gait test scores obtained using a tri-axial accelerometer can detect subjective improvement in gait as determined by the patient after carotid endarterectomy (CEA). Each patient undergoing CEA for ipsilateral internal carotid artery stenosis determined whether their gait was subjectively improved at six months after CEA when compared with preoperatively. Gait testing using a tri-axial accelerometer was also performed preoperatively and six months postoperatively. Twelve (15%) of 79 patients reported subjectively improved gait. Areas under the receiver operating characteristic curve for differences between pre- and postoperative test values in stride time, cadence, and ground floor reaction for detecting subjectively improved gait were 0.995 (95% confidence interval (CI), 0.945-1.000), 0.958 (95%CI, 0.887-0.990), and 0.851 (95%CI, 0.753-0.921), respectively. Cut-off points for value differences in detecting subjectively improved gait were identical to mean -1.7 standard deviation (SD) for stride time, mean +1.6 SD for cadence, and mean +0.4 SD for ground floor reaction of control values from normal subjects. Objective gait test scores obtained using the tri-axial accelerometer can detect subjective gait improvements after CEA. When determining significant postoperative improvements in gait using a tri-axial accelerometer, optimal cut-off points for each test value can be defined.

Percentiles and Reference Values for Accelerometric Gait Assessment in Women Aged 50-80 Years

Background: The identification of factors that alter postural stability is fundamental in the design of interventions to maintain independence and mobility. This is especially important for women because of their longer life expectancy and higher incidence of falls than in men. We constructed the percentile box charts and determined the values of reference for the accelerometric assessment of the gait in women. Methods: We used a cross-sectional study with 1096 healthy adult women, who were asked to walk a distance of 20 m three times. Results: In all of the variables, a reduction in the magnitude of accelerations was detected as the age of the group advanced. The box charts show the amplitude of the interquartile ranges, which increases as the age of the participants advances. In addition, the interquartile ranges were greater in the variables that refer to the maximum values of the accelerations. Conclusions: The values obtained can be used to assess changes in gait due to aging, trauma and orthopaedic alterations that may alter postural stability and neurodegenerative processes that increase the risk of falling.

Characterization of Gait and Postural Regulation in Late-Onset Pompe Disease

Pompe disease is a multisystemic disorder with the hallmark of progressive skeletal muscle weakness that often results in difficulties in walking and balance. However, detailed characterization of gait and postural regulation with this disease is lacking. The objective of this investigation was to determine if differences exist between the gait and postural regulation of LOPD patients and a matched control group. The gaits of 16 patients with LOPD were assessed using a gait analysis mobile system (RehaGait) and a dynamometric treadmill (FDM-T 1.8). The Interactive Balance System (IBS) was used to evaluate postural regulation and stability. All measures were compared to individual reference data. Demographic (age, gender), morphological (body height, body mass) and clinical data (muscle strength according to the Medical Research Council Scale (MRC Scale), as well as the 6-min walking test and a 10-m fast walk) were also recorded. Compared to individual reference data, LOPD patients presented with reduced gait velocity, cadence and time in single stand. A total of 87% of LOPD patients had abnormalities during posturographic analysis presenting with differences in postural subsystems. This study provides objective data demonstrating impaired gait and posture in LOPD patients. For follow-up analysis and as outcome measurements during medical or physiotherapeutic interventions, the findings of this investigation may be useful.

Measuring gait kinematics in patients with severe hip osteoarthritis using wearable sensors

BACKGROUND

The popularity of inertial sensors in gait analysis is steadily rising. To date, an application of a wearable inertial sensor system for assessing gait in hip osteoarthritis (OA) has not been reported.

RESEARCH QUESTION

Can the known kinematic differences between patients with hip OA and asymptomatic control subjects be measured using the inertial sensor system RehaGait®?

METHODS

The patients group consisted of 22 patients with unilateral hip OA scheduled for total hip replacement. Forty-five age matched healthy control subjects served as control group. All subjects walked for a distance of 20 m at their self-selected speed. Spatiotemporal parameters and sagittal kinematics at the hip, knee, and ankle including range of motion (ROM) were measured using the RehaGait® system.

RESULTS

Patients with hip OA walked at a slower walking speed (-0.18 m/s, P < 0.001) and with shorter stride length (-0.16 m, P < 0.001), smaller hip ROM during stance (-11.6°, P < 0.001) and swing (-11.3°, P < 0.001) and smaller knee ROM during terminal stance and swing (-9.0° and-11.5°, P < 0.001). Patients had a smaller hip ROM during stance and swing and smaller knee ROM during terminal stance and swing in the affected compared to the unaffected side (P < 0.001).

SIGNIFICANCE

The differences in spatiotemporal and kinematic gait parameters between patients with hip OA and age matched control subjects assessed using the inertial sensor system agree with those documented for camera-based systems. Hence, the RehaGait® system can measure gait kinematics characteristic for hip OA, and its use in daily clinical practice is feasible.

Improvement in gait function after carotid endarterectomy is associated with postoperative recovery in perfusion and neurotransmitter receptor function in the motor-related cerebral cortex: a 123I-iomazenil SPECT study

OBJECTIVE

Carotid endarterectomy (CEA) often restores cerebral perfusion and neurotransmitter receptor function, which is seen on early and late images, respectively, on brain I-iomazenil single-photon emission computed tomography (SPECT). The reliability of gait-related parameters obtained using a triaxial accelerometer, a portable device for gait assessment, has been confirmed with test-retest measurements. The purpose of the present prospective cohort study was to determine whether improvement in gait function after CEA is associated with postoperative recovery in perfusion and neurotransmitter receptor function in the motor-related cerebral cortex.

METHODS

Gait testing using a triaxial accelerometer was performed preoperatively and 6 months postoperatively in 64 patients undergoing CEA for ipsilateral internal carotid artery stenosis (≥70%). I-iomazenil SPECT was also performed with scanning within 30 min (early images) and at 180 min (late images) after tracer administration before and after surgery. SPECT data were analyzed using a three-dimensional stereotactic surface projection, and motor (Brodmann 4) and premotor (Brodmann 6) cortexes in each hemisphere were combined and defined as the motor-related cortex.

RESULTS

Based on preoperative and postoperative gait testing, seven patients (11%) showed postoperative improved gait. Logistic regression analysis revealed that postoperative increase in I-iomazenil uptake in the motor-related cortex ipsilateral to surgery on early [95% confidence interval (CI), 4.32-365.21; P = 0.0477) or late (95% CI, 9.45-1572.57; P = 0.0173) images was an independent predictor of postoperative improved gait.

CONCLUSIONS

Improvement in gait function after CEA is associated with postoperative recovery in perfusion and neurotransmitter receptor function in the motor-related cerebral cortex.

Kinematics and temporospatial parameters during gait from inertial motion capture in adults with and without HIV: a validity and reliability study

BACKGROUND

Inertial measurement unit (IMU)-based motion capture systems are gaining popularity for gait analysis outside laboratories. It is important to determine the performance of such systems in specific patient populations. We aimed to validate and determine within-day reliability of an IMU system for measuring lower limb gait kinematics and temporal-spatial parameters (TSP) in people with and without HIV.

METHODS

Gait was recorded in eight adults with HIV (PLHIV) and eight HIV-seronegative participants (SNP), using IMUs and optical motion capture (OMC) simultaneously. Participants performed six gait trials. Fifteen TSP and 28 kinematic angles were extracted. Intraclass correlations (ICC), root-mean-square error (RMSE), mean absolute percentage error and Bland-Altman analyses were used to assess concurrent validity of the IMU system (relative to OMC) separately in PLHIV and SNP. IMU reliability was assessed during within-session retest of trials. ICCs were used to assess relative reliability. Standard error of measurement (SEM) and percentage SEM were used to assess absolute reliability.

RESULTS

Between-system TSP differences demonstrated acceptable-to-excellent ICCs (0.71-0.99), except for double support time and temporophasic parameters (< 0.60). All TSP demonstrated good mean absolute percentage errors (≤7.40%). For kinematics, ICCs were acceptable to excellent (0.75-1.00) for all but three range of motion (ROM) and four discrete angles. RMSE and bias were 0.0°-4.7° for all but two ROM and 10 discrete angles. In both groups, TSP reliability was acceptable to excellent for relative (ICC 0.75-0.99) (except for one temporal and two temporophasic parameters) and absolute (%SEM 1.58-15.23) values. Reliability trends of IMU-measured kinematics were similar between groups and demonstrated acceptable-to-excellent relative reliability (ICC 0.76-0.99) and clinically acceptable absolute reliability (SEM 0.7°-4.4°) for all but two and three discrete angles, respectively. Both systems demonstrated similar magnitude and directional trends for differences when comparing the gait of PLHIV with that of SNP.

CONCLUSIONS

IMU-based gait analysis is valid and reliable when applied in PLHIV; demonstrating a sufficiently low precision error to be used for clinical interpretation (< 5° for most kinematics; < 20% for TSP). IMU-based gait analysis is sensitive to subtle gait deviations that may occur in PLHIV.

Classification of Neurological Patients to Identify Fallers Based on Spatial-Temporal Gait Characteristics Measured by a Wearable Device

Neurological patients can have severe gait impairments that contribute to fall risks. Predicting falls from gait abnormalities could aid clinicians and patients mitigate fall risk. The aim of this study was to predict fall status from spatial-temporal gait characteristics measured by a wearable device in a heterogeneous population of neurological patients. Participants (n = 384, age 49–80 s) were recruited from a neurology ward of a University hospital. They walked 20 m at a comfortable speed (single task: ST) and while performing a dual task with a motor component (DT1) and a dual task with a cognitive component (DT2). Twenty-seven spatial-temporal gait variables were measured with wearable sensors placed at the lower back and both ankles. Partial least square discriminant analysis (PLS-DA) was then applied to classify fallers and non-fallers. The PLS-DA classification model performed well for all three gait tasks (ST, DT1, and DT2) with an evaluation of classification performance Area under the receiver operating characteristic Curve (AUC) of 0.7, 0.6 and 0.7, respectively. Fallers differed from non-fallers in their specific gait patterns. Results from this study improve our understanding of how falls risk-related gait impairments in neurological patients could aid the design of tailored fall-prevention interventions.

Gait Event Detection for Stroke Patients during Robot-Assisted Gait Training

Functional electrical stimulation and robot-assisted gait training are techniques which are used in a clinical routine to enhance the rehabilitation process of stroke patients. By combining these technologies, therapy effects could be further improved and the rehabilitation process can be supported. In order to combine these technologies, a novel algorithm was developed, which aims to extract gait events based on movement data recorded with inertial measurement units. In perspective, the extracted gait events can be used to trigger functional electrical stimulation during robot-assisted gait training. This approach offers the possibility of equipping a broad range of potential robot-assisted gait trainers with functional electrical stimulation. In particular, the aim of this study was to test the robustness of the previously developed algorithm in a clinical setting with patients who suffered a stroke. A total amount of N = 10 stroke patients participated in the study, with written consent. The patients were assigned to two different robot-assisted gait trainers (Lyra and Lokomat) according to their performance level, resulting in five recording sessions for each gait-trainer. A previously developed algorithm was applied and further optimized in order to extract the gait events. A mean detection rate across all patients of 95.8% ± 7.5% for the Lyra and 98.7% ± 2.6% for the Lokomat was achieved. The mean type 1 error across all patients was 1.0% ± 2.0% for the Lyra and 0.9% ± 2.3% for the Lokomat. As a result, the developed algorithm was robust against patient specific movements, and provided promising results for the further development of a technique that can detect gait events during robot-assisted gait training, with the future aim to trigger functional electrical stimulation.

Kinematic changes in patients with severe knee osteoarthritis are a result of reduced walking speed rather than disease severity

BACKGROUND

Kinematic changes in patients with knee osteoarthritis (OA) have been extensively studied. Concerns have been raised whether the measured spatiotemporal and kinematic alterations are associated with disease progression or merely a result of reduced walking speed.

RESEARCH QUESTION

The purpose of this study was to investigate the effect of walking speed on kinematic parameters in patients with knee OA using statistical parametric mapping (SPM).

METHODS

Twenty-three patients with unilateral knee OA scheduled for a total knee replacement and 28 age matched control subjects were included in this study. Spatiotemporal parameters and sagittal plane kinematics were measured in the hip, knee, and ankle using the inertial sensors system RehaGait® while walking at a self-selected normal (patients and controls) and slow walking speed (controls) for a distance of 20 m. Gait parameters were compared between groups for self-selected walking speed and for matched walking speed using SPM with independent sample t tests.

RESULTS

At self-selected walking speed, patients had significantly lower knee flexion during stance (maximum difference, -6.8°) and during swing (-11.0°), as well as higher ankle dorsiflexion during stance phase (+12.5°) and lower peak hip extension at the end of stance compared to controls (+4.2°). At matched speed, there were no significant differences in joint kinematics between groups.

SIGNIFICANCE

Differences in sagittal plane gait kinematics between patients with knee OA and asymptomatic controls appear to be mainly a result of reduced walking speed. These results emphasize the importance of considering walking speed in research on gait kinematics in patients with knee OA and in clinical trials using gait parameters as outcome measures.

Accuracy Verification of Spatio-Temporal and Kinematic Parameters for Gait Using Inertial Measurement Unit System

Inertial measurement unit systems are wearable sensors that can measure the movement of a human in real-time with relatively little space and high portability. The purpose of this study was to investigate the accuracy of the inertial measurement unit (IMU) system for gait analysis by comparing it with measurements obtained using an optical motion capture (OMC) system. To compare the accuracies of these two different motion capture systems, the Spatio-temporal and kinematic parameters were measured in young adults during normal walking. Thirty healthy participants participated in the study. Data were collected while walking 5 strides on a 7 m walkway at a self-selected speed. Results of gait analysis showed that the Spatio-temporal (stride time, stride length, cadence, step length) and kinematic (knee joint peak to peak of movement) parameters were not significantly different in the participant. Spatio-temporal and kinematic parameters of the two systems were compared using the Bland–Altman method. The results obtained showed that the measurements of Spatio-temporal and kinematic parameters of gait by the two systems were similar, which suggested that IMU and OMC systems could be used interchangeably for gait measurements. Therefore, gait analysis performed using the wearable IMU system might efficiently provide gait measurements and enable accurate analysis.

The Coefficient of Variation of Step Time Can Overestimate Gait Abnormality: Test-Retest Reliability of Gait-Related Parameters Obtained with a Tri-Axial Accelerometer in Healthy Subjects

The aim of this study was to investigate whether variation in gait-related parameters among healthy participants could help detect gait abnormalities. In total, 36 participants (21 men, 15 women; mean age, 35.7 ± 9.9 years) performed a 10-m walk six times while wearing a tri-axial accelerometer fixed at the L3 level. A second walk was performed ≥1 month after the first (mean interval, 49.6 ± 7.6 days). From each 10-m data set, the following nine gait-related parameters were automatically calculated: assessment time, number of steps, stride time, cadence, ground force reaction, step time, coefficient of variation (CV) of step time, velocity, and step length. Six repeated measurement values were averaged for each gait parameter. In addition, for each gait parameter, the difference between the first and second assessments was statistically examined, and the intraclass correlation coefficient (ICC) was calculated with the level of significance set at p < 0.05. Only the CV of step time showed a significant difference between the first and second assessments (p = 0.0188). The CV of step time also showed the lowest ICC, at <0.50 (0.425), among all parameters. Test-retest results of gait assessment using a tri-axial accelerometer showed sufficient reproducibility in terms of the clinical evaluation of all parameters except the CV of step time.

Severity of degenerative lumbar spinal stenosis affects pelvic rigidity during walking

BACKGROUND CONTEXT

To understand the role of compensation mechanisms in the development and treatment of symptomatic degenerative lumbar spinal stenosis (DLSS), pelvic stability during walking should be objectively assessed in the context of clinical parameters.

PURPOSE

To determine the association among duration of symptoms, lumbar muscle atrophy, disease severity, pelvic stability during walking, and surgical outcome in patients with DLSS scheduled for decompression surgery.

STUDY DESIGN/SETTING

Prospective observational study with intervention.

PATIENT SAMPLE

Patients with symptomatic DLSS.

OUTCOME MEASURES

Oswestry Disability Index score; duration of symptoms; lumbar muscle atrophy; severity grade; pelvis rigidity during walking.

METHODS

Patients with symptomatic DLSS were analyzed on the day before surgery and 10 weeks and 12 months postoperatively. Duration of symptoms was categorized as: <2years, <5years, and >5years. Muscle atrophy at the stenosis level was categorized according to Goutallier. Bilateral cross-sectional areas of the erector spinae and psoas muscles were quantified from magnetic resonance imaging. Stenosis grade was assessed using the Schizas classification. Pelvic tilt was measured in standing radiographs. Pelvic rigidity during walking was assessed as root mean square of the pelvic acceleration in each direction (anteroposterior, mediolateral, and vertical) normalized to walking speed measured using an inertial sensor attached to the skin between the posterior superior iliac spine.

RESULTS

Body mass index but not duration of symptoms, lumbar muscle atrophy, pelvic rigidity, and stenosis grade explained changes in Oswestry Disability Index from before to after surgery. Patients with greater stenosis grade had greater pelvic rigidity during walking. Lumbar muscle atrophy did not correlate with pelvic rigidity during walking. Patients with lower stenosis grade had greater muscle atrophy and patients with smaller erector spinae and psoas muscle cross-sectional areas had a greater pelvis tilt.

CONCLUSIONS

Greater pelvic rigidity during walking may represent a compensatory mechanism of adopting a protective body position to keep the spinal canal more open during walking and hence reduce pain. Pelvic rigidity during walking may be a useful screening parameter for identifying early compensating mechanisms. Whether it can be used as a parameter for personalized treatment planning or outcome prognosis necessitates further evaluation.

Accurate Ambulatory Gait Analysis in Walking and Running Using Machine Learning Models

Wearable sensors have been proposed as alternatives to traditional laboratory equipment for low-cost and portable real-time gait analysis in unconstrained environments. However, the moderate accuracy of these systems currently limits their widespread use. In this paper, we show that support vector regression (SVR) models can be used to extract accurate estimates of fundamental gait parameters (i.e., stride length, velocity, and foot clearance), from custom-engineered instrumented insoles (SportSole) during walking and running tasks. Additionally, these learning-based models are robust to inter-subject variability, thereby making it unnecessary to collect subject-specific training data. Gait analysis was performed in N=14 healthy subjects during two separate sessions, each including 6-minute bouts of treadmill walking and running at different speeds (i.e., 85% and 115% of each subject's preferred speed). Gait metrics were simultaneously measured with the instrumented insoles and with reference laboratory equipment. SVR models yielded excellent intraclass correlation coefficients (ICC) in all the gait parameters analyzed. Percentage mean absolute errors (MAE%) in stride length, velocity, and foot clearance obtained with SVR models were 1.37%±0.49%, 1.23%±0.27%, and 2.08%±0.72% for walking, 2.59%±0.64%, 2.91%±0.85%, and 5.13%±1.52% for running, respectively. These findings provide evidence that machine learning regression is a promising new approach to improve the accuracy of wearable sensors for gait analysis.

Physical Activity and Physiological Profiles of Elite International Female Field Hockey Players Across the Quarters of Competitive Match Play

McGuinness, A, Malone, S, Hughes, B, and Collins, K. Physical activity and physiological profiles of elite international female field hockey players across the quarters of competitive match play. J Strength Cond Res 33(9): 2513-2522, 2019-The aim of the current investigation was to quantify the physical and physiological demands of elite international female field hockey across the quarters of match-play. Twenty-seven elite international female field hockey outfield players (23 ± 3 years; 162.6 ± 13.0 cm; 66.0 ± 6.0 kg) participated in the current observational study during the 2016-2017 season. Participants were monitored using global positioning system technology and HR monitors. Players were categorized based on 3 different playing positions. Activity was categorized into total distance (in meters), relative total distance (m·min), low-, moderate-, and high-intensity distance (m), maximum velocity (km·h), and percentage maximal velocity (%). Physiological demands were quantified through players peak heart rate (HRPeak), which was classified based on the player's individual HRmax determined using a Yo-Yo intermittent recovery level 1 test. Players spent on average 38 ± 8 minutes in match play. The total distance covered was 4,847 ± 583 m (127.6 ± 15.6 m·min). Defenders covered a greater total distance across all 3 positions (p = ≤ 0.05). The midfielders covered a greater moderate-intensity distance (p ≤ 0.001), whereas the forwards covered more high-intensity distance (p ≤ 0.001). The HRpeak of the players was 198 ± 4 b·min with a mean exercise intensity of 95 ± 1% HRmax. The time spent >70% HRmax decreased significantly across the quarters (p = 0.01, η = 0.03). Defenders were found to spend more time >85% HRmax when compared with other positions (p ≤ 0.001, η = 0.28). The current study provides normative data that coaches should consider when developing training drills to better optimize the positional physical and physiological activity profiles that best replicate match play.

Towards an Inertial Sensor-Based Wearable Feedback System for Patients after Total Hip Arthroplasty: Validity and Applicability for Gait Classification with Gait Kinematics-Based Features

Patients after total hip arthroplasty (THA) suffer from lingering musculoskeletal restrictions. Three-dimensional (3D) gait analysis in combination with machine-learning approaches is used to detect these impairments. In this work, features from the 3D gait kinematics, spatio temporal parameters (Set 1) and joint angles (Set 2), of an inertial sensor (IMU) system are proposed as an input for a support vector machine (SVM) model, to differentiate impaired and non-impaired gait. The features were divided into two subsets. The IMU-based features were validated against an optical motion capture (OMC) system by means of 20 patients after THA and a healthy control group of 24 subjects. Then the SVM model was trained on both subsets. The validation of the IMU system-based kinematic features revealed root mean squared errors in the joint kinematics from 0.24° to 1.25°. The validity of the spatio-temporal gait parameters (STP) revealed a similarly high accuracy. The SVM models based on IMU data showed an accuracy of 87.2% (Set 1) and 97.0% (Set 2). The current work presents valid IMU-based features, employed in an SVM model for the classification of the gait of patients after THA and a healthy control. The study reveals that the features of Set 2 are more significant concerning the classification problem. The present IMU system proves its potential to provide accurate features for the incorporation in a mobile gait-feedback system for patients after THA.