The linear-envelope peak (LEP) of surface EMG signal is widely used in gait analysis to characterize muscular activity, especially in clinics.
This study is designed to evaluate LEP accuracy in ...identifying muscular activation and assessing activation timing during walking.
Surface EMG signals from gastrocnemius lateralis (GL) and tibialis anterior (TA) were analyzed in 100 strides per subject (31 healthy subjects) during ground walking. Signals were full-wave rectified and low-pass filtered (cut-off frequency=5 Hz) to extract the linear envelope. LEP accuracy in identifying muscle activations and the associated error in peak detection were assessed by direct comparison with a reference method based on wavelet transform. LEP accuracy in identifying the timing of higher signalenergy levels was also assessed, increasing the reference-algorithm selectivity.
The detection error (percentage number of times when LEP falls outside the correspondent reference activation interval) is close to zero. Detection error increases up to 70% for intervals including only signal energy higher than 90% of energy peak. Mean absolute error (MAE, the absolute value of the distance between LEP timing and the correspondent actual timing of the sEMG-signal peak computed by reference algorithm) is 54.1±20.0 ms. Detection error and MAE are significantly higher (p<0.05) in TA data compared to GL signals. Differences among MAE values detected adopting different values for LE cut-off frequency are not statistically significant.
LEP was found to be accurate in identifying the number of muscle activations during walking. However, the use of LEP to assess the timing of highest sEMG-signal energy (signal peak) should be considered carefully. Indeed, it could introduce a relevant inaccuracy in muscle-activation identification and peak-timing quantification. The type of muscle to analyze could also influence LEP performances, while the cut-off frequency chosen for envelope extraction appears to have a limited impact.
•Linear envelope peak (LEP) is widely used to identify muscle activation during gait.•LEP accuracy is tested in 3100 strides of 31 subjects vs. a wavelet-based algorithm.•LEP provides an accurate identification of the number of activations in a stride.•The use of LEP introduces a relevant error in the quantification of EMG-peak timing.•LEP performances are also influenced by the type of muscle being analyzed.
Clinical gait analysis (CGA) is a systematic approach to comprehensively evaluate gait patterns, quantify impairments, plan targeted interventions, and evaluate the impact of interventions. However, ...international standards for CGA are currently lacking, resulting in various national initiatives. Standards are important to ensure safe and effective healthcare practices and to enable evidence-based clinical decision-making, facilitating interoperability, and reimbursement under national healthcare policies. Collaborative clinical and research work between European countries would benefit from common standards.
This study aimed to review the current laboratory practices for CGA in Europe.
A comprehensive survey was conducted by the European Society for Movement Analysis in Adults and Children (ESMAC), in close collaboration with the European national societies. The survey involved 97 gait laboratories across 16 countries. The survey assessed several aspects related to CGA, including equipment used, data collection, processing, and reporting methods.
There was a consensus between laboratories concerning the data collected during CGA. The Conventional Gait Model (CGM) was the most used biomechanical model for calculating kinematics and kinetics. Respondents also reported the use of video recording, 3D motion capture systems, force plates, and surface electromyography. While there was a consensus on the reporting of CGA data, variations were reported in training, documentation, data preprocessing and equipment maintenance practices.
The findings of this study will serve as a foundation for the development of standardized guidelines for CGA in Europe.
•Survey report on current Clinical Gait Analysis (CGA) practices across Europe.•Good agreement on data included in CGA with video, kinematics, kinetics and EMG.•Training, documentation, maintenance, reliability evaluations vary between laboratories.•New gait analysis tools have limited adoption in CGA.•Results set foundation for a Delphi process to define European CGA standards.
Accurate detection of gait events is crucial for gait analysis, enabling the assessment of gait patterns and abnormalities. Inertial measurement unit (IMU) sensors have gained traction for event ...detection, mainly focusing on initial contact (IC) and toe-off (TO) events. However, effective detection of other key events such as heel rise (HR), feet adjacent (FA), and tibia vertical (TBV) is essential for comprehensive gait analysis.
Can a novel IMU-based method accurately detect HR, TO, FA, and TBV events, and how does its performance compare with existing methods?
We developed and validated an IMU-based method using cumulative mediolateral shank angular velocity (CSAV) for event detection. A dataset of nearly 25,000 gait cycles from healthy adults walking at varying speeds and footwear conditions was used for validation. The method’s accuracy was assessed against force plate and motion capture data and compared with existing TO detection methods.
The CSAV method demonstrated high accuracy in detecting TO, FA, and TBV events and moderate accuracy in HR event detection. Comparisons with existing TO detection methods showcased superior performance. The method's stability across speed and shoe variations underscored its robustness.
This study introduces a highly accurate IMU-based method for detecting gait events needed to divide the gait cycle into seven phases. The effectiveness of the CSAV method in capturing essential events across different scenarios emphasizes its potential applications. Although HR event detection can be further improved, the precision of the CSAV method in TO, FA, and TBV detection advance the field. This study bridges a critical gap in IMU-based gait event detection by introducing a method for subdividing the swing phase into its subphases. Further research can focus on refining HR detection and expanding the method’s utility across diverse gait contexts, thereby enhancing its clinical and scientific significance.
•15 healthy participants, 24,519 steps, and 9 scenarios.•Using shank angular velocity, novel method detects gait events with precision.•Validated for initial contact, heel rise, toe-off, feet adjacent, and tibia vertical.•Bland-Altman plots, ICC scores show high agreement with gold standard techniques.
Mobile, sensor-based gait analysis in Parkinson’s disease (PD) facilitates the objective measurement of gait parameters in cross-sectional studies. Besides becoming outcome measures for clinical ...studies, the application of gait parameters in personalized clinical decision support is limited. Therefore, the aim of this study was to evaluate whether the individual response of PD patients to dopaminergic treatment may be measured by sensor-based gait analysis. 13 PD patients received apomorphine every 15 min to incrementally increase the bioavailable apomorphine dose. Motor performance (UPDRS III) was assessed 10 min after each apomorphine injection. Gait parameters were obtained after each UPDRS III rating from a 2 × 10 m gait sequence, providing 41.2 ± 9.2 strides per patient and injection. Gait parameters and UPDRS III ratings were compared cross-sectionally after apomorphine titration, and more importantly between consecutive injections for each patient individually. For the individual response, the effect size Cohen’s d for gait parameter changes was calculated based on the stride variations of each gait sequence after each injection. Cross-sectionally, apomorphine improved stride speed, length, gait velocity, maximum toe clearance, and toe off angle. Between injections, the effect size for individual changes in stride speed, length, and maximum toe clearance correlated to the motor improvement in each patient. In addition, significant changes of stride length between injections were significantly associated with UPDRS III improvements. We therefore show, that sensor-based gait analysis provides objective gait parameters that support clinical assessment of individual PD patients during dopaminergic treatment. We propose clinically relevant instrumented gait parameters for treatment studies and especially clinical care.
This study aimed to develop and evaluate a costeffective Inertial Measurement Unit (IMU) system for gait analysis, comparing its performance with the Vicon system and the VideoPose3D algorithm. The ...system comprises five calibrated sensors and a mobile app to measure lower body orientation during gait and stair climbing. Eight healthy participants were involved in the experiment, each performing ten repetitions to analyze hip and knee flexion angles. The IMU system demonstrated significantly lower mean square error than deep learning-based approaches and comparable results to the Vicon system, indicating its potential for clinical and research applications.
The aim of this study is to evaluate if Kinect is a valid and reliable clinical gait analysis tool for children with cerebral palsy (CP), and whether linear regression and long short-term memory ...(LSTM) recurrent neural network methods can improve its performance. A gait analysis was conducted on ten children with CP, on two occasions. Lower limb joint kinematics computed from the Kinect and a traditional marker-based Motion Analysis system were investigated by calculating the root mean square errors (RMSE), the coefficients of multiple correlation (CMC), and the intra-class correlation coefficients (ICC
). Results showed that the Kinect-based kinematics had an overall modest to poor correlation (CMC-less than 0.001 to 0.70) and an angle pattern similarity with Motion Analysis. After the calibration, RMSE on every degree of freedom decreased. The two calibration methods indicated similar levels of improvement in hip sagittal (CMC-0.81 ± 0.10 vs. 0.75 ± 0.22)/frontal (CMC-0.41 ± 0.35 vs. 0.42 ± 0.37) and knee sagittal kinematics (CMC-0.85±0.07 vs. 0.87 ± 0.12). The hip sagittal (CMC-0.97±0.05) and knee sagittal (CMC-0.88 ± 0.12) angle patterns showed a very good agreement over two days. Modest to excellent reliability (ICC
-0.45 to 0.93) for most parameters renders it feasible for observing ongoing changes in gait kinematics.
Quantitative gait analysis (QGA) has the potential to support clinician decision-making. However, it is not yet widely accepted in practice. Evidence for clinical efficacy (i.e., efficacy and ...effectiveness), as well as a users' perspective on using the technology in clinical practice (e.g., ease of use and usefulness) can help impact their widespread adoption.
To synthesize the literature on the clinical efficacy and clinician perspectives on the use of gait analysis technologies in the clinical care of adult populations.
This scoping review followed the Joanna Briggs Institute (JBI) methodology for scoping reviews. We included peer-reviewed and gray literature (i.e., conference abstracts). A search was conducted in MEDLINE (Ovid), CENTRAL (Ovid), EMBASE (Ovid), CINAHL (EBSCO) and SPORTDiscus (EBSCO). Included full-text studies were critically appraised using the JBI critical appraisal tools.
A total of 15 full-text studies and two conference abstracts were included in this review. Results suggest that QGA technologies can influence decision-making with some evidence to suggest their role in improving patient outcomes. The main barrier to ease of use was a clinician's lack of data expertise, and main facilitator was receiving support from staff. Barriers to usefulness included challenges finding suitable reference data and data accuracy, while facilitators were enhancing patient care and supporting clinical decision-making.
This review is the first step to understanding how QGA technologies can optimize clinical practice. Many gaps in the literature exist and reveal opportunities to improve the clinical adoption of gait analysis technologies. Further research is needed in two main areas: 1) examining the clinical efficacy of gait analysis technologies and 2) gathering clinician perspectives using a theoretical model like the Technology Acceptance Model to guide study design. Results will inform research aimed at evaluating, developing, or implementing these technologies.
This work was supported by the Walter and Maria Schroeder Institute for Brain Innovation and Recovery and AGE-WELL Graduate Student Award in Technology and Aging 2021,2022.
For gait classification, hoof-on and hoof-off events are fundamental locomotion characteristics of interest. These events can be measured with inertial measurement units (IMUs) which measure the ...acceleration and angular velocity in three directions. The aim of this study was to present two algorithms for automatic detection of hoof-events from the acceleration and angular velocity signals measured by hoof-mounted IMUs in walk and trot on a hard surface. Seven Warmblood horses were equipped with two wireless IMUs, which were attached to the lateral wall of the right front (RF) and hind (RH) hooves. Horses were walked and trotted on a lead over a force plate for internal validation. The agreement between the algorithms for the acceleration and angular velocity signals with the force plate was evaluated by Bland Altman analysis and linear mixed model analysis. These analyses were performed for both hoof-on and hoof-off detection and for both algorithms separately. For the hoof-on detection, the angular velocity algorithm was the most accurate with an accuracy between 2.39 and 12.22 ms and a precision of around 13.80 ms, depending on gait and hoof. For hoof-off detection, the acceleration algorithm was the most accurate with an accuracy of 3.20 ms and precision of 6.39 ms, independent of gait and hoof. These algorithms look highly promising for gait classification purposes although the applicability of these algorithms should be investigated under different circumstances, such as different surfaces and different hoof trimming conditions.
Objective: This paper describes how non-invasive wearable sensors can be used in combination with deep learning to classify artificially induced gait alterations without the requirement for a medical ...professional or gait analyst to be present. This approach is motivated by the goal of diagnosing gait abnormalities on a symptom-by-symptom basis, irrespective of other neuromuscular movement disorders the patients may be affected by. This could lead to improvements in treatment and offer a greater insight into movement disorders. Methods: In-shoe pressure was measured for 12 able-bodied participants, each subject to eight artificially induced gait alterations, achieved by modifying the underside of the shoe. The data were recorded at 100 Hz over 2520 data channels and were analyzed using the deep learning architecture and the long term short term memory networks. Additionally, the rationale for the decision-making process of these networks was investigated. Conclusion: Long term short term memory networks are applicable to the classification of the gait function. The classifications can be made using only 2 s of sparse data (82.0% accuracy over 96 000 instances of test data) from participants who were not a part of the training set. Significance: This paper provides potential for the gait function to be accurately classified using non-invasive techniques, and at more regular intervals, outside of a clinical setting, without the need for healthcare professionals to be present.
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