Gait Events Research Articles

Taking Locomotion Mode as Prior: One Algorithm-Enabled Gait Events and Kinematics Prediction on Various Terrains

Both the discrete motion states and continuous joint kinematics are essential for controlling assistive robots under changeable environmental conditions. However, few studies investigate both the discrete and continuous motion intents. This paper is the first work to propose an end-to-end motion intent decoding method that integrates recognition of discrete locomotion modes and prediction of continuous joint kinematics and gait events. First, we propose a data-driven approach to segment the transitional periods of the adjacent locomotion modes and determine their boundaries. Second, we build a CNN-LSTM network that utilizes locomotion mode classification as the prior of joint kinematics and gait events prediction and thus decodes discrete and continuous motion intents. Finally, we evaluate our method through extensive experiments and comparisons. The proposed method for locomotion mode recognition achieves an accuracy of 98.73% for steady-state and 97.53% for transitional periods. For knee angle prediction, the method achieves the ahead-of-time prediction with NRMSE lower than 8.41%, R-value higher than 0.93, and R-square higher than 0.88 for various prediction time. For gait event prediction, it predicts the future timing of events with an error of 8.46 ms for initial contact, 32.76 ms for heel off, and 8.91 ms for toe off. This study reveals the feasibility of predicting locomotion-based kinematics and gait events on various terrains. The rich motion intents studied in this work highlight the potential of a more detailed lower-limb rehabilitation system.

Real-Time Gait Event Detection with Adaptive Frequency Oscillators From a Single Head-Mounted IMU

Accurate real-time gait event detection is the basis for the development of new gait rehabilitation techniques, especially when utilizing robotics or virtual reality (VR). The recent emergence of affordable wearable technologies, especially inertial measurement units (IMUs), has brought forth various new methods and algorithms for gait analysis. In this paper, we highlight some advantages of using adaptive frequency oscillators (AFOs) over traditional gait event detection algorithms, implemented a real-time AFO-based algorithm that estimates the gait phase from a single head-mounted IMU, and validated our method on a group of healthy subjects. Gait event detection was accurate at two different walking speeds. The method was reliable for symmetric, but not asymmetric gait patterns. Our method could prove especially useful in VR applications since a head-mounted IMU is already an integral part of commercial VR products.

Abnormal gait partitioning and real-time recognition of gait phases in children with cerebral palsy

ObjectiveSince the gait of children with cerebral palsy (CP) lacks distinct foot event characteristics, it’s difficult to accurately distinguish the key gait events, especially in the stance phase. To solve the problem of gait phase detection for children with CP, a unique gait classification system is designed to detect the walking maneuvers during a gait cycle. This system can detect all gait phases of CP patients, and send the kinematic information to a rule-based algorithm through a series of sensors installed in the legs to identify the salient features of the signal. MethodsIn the light of the quasi-stiffness gait phase transformation as the classification guide, two sets of motion signal feature sets are extracted from the leg feature signals of 10 CP children, and two gait stage recognition algorithms are proposed to divide the six gait sub-phases. ResultsThe proposed algorithms of peak-zero-based angular velocity and thresholding-based angular realize the effective recognition of gait phases, especially for the stance phase. ConclusionThe proposed methods have the potential to obtain correct decoding of motion information and recognize gait phases. Significance: This study provides a new idea to solve the problem of difficult identification of gait phase for children with CP, which can be applied to a behavior-driven lower limb exoskeleton robot system for assisting walking and gait correction.

A pilot case series for concurrent validation of inertial measurement units to motion capture in individuals who use unilateral lower-limb prostheses.

Inertial measurement units (IMUs) may be viable options to collect gait data in clinics. This study compared IMU to motion capture data in individuals who use unilateral lower-limb prostheses. Participants walked with lower-body IMUs and reflective markers in a motion analysis space. Sagittal plane hip, knee, and ankle waveforms were extracted for the entire gait cycle. Discrete points of peak flexion, peak extension, and range of motion were extracted from the waveforms. Stance times were also extracted to assess the IMU software's accuracy at detecting gait events. IMU and motion capture-derived data were compared using absolute differences and root mean square error (RMSE). Five individuals (n = 3 transtibial; n = 2 transfemoral) participated. IMU prosthetic limb data was similar to motion capture (RMSE: waveform ≤4.65°; discrete point ≤9.04°; stance ≤0.03s). However, one transfemoral participant had larger differences at the microprocessor knee joint (RMSE: waveform ≤15.64°; discrete ≤29.21°) from IMU magnetometer interference. Intact limbs tended to have minimal differences between IMU and motion capture data (RMSE: waveform ≤6.33°; discrete ≤9.87°; stance ≤0.04s). Findings from this pilot study suggest IMUs have the potential to collect data similar to motion capture systems in sagittal plane kinematics and stance time.

Gait Event Detection with Proprioceptive Force Sensing in a Powered Knee-Ankle Prosthesis: Validation over Walking Speeds and Slopes.

Many powered prosthetic devices use load cells to detect ground interaction forces and gait events. These sensors introduce additional weight and cost in the device. Recent proprioceptive actuators enable an algebraic relationship between actuator torques and ground contact forces. This paper presents a proprioceptive force sensing paradigm which estimates ground reaction forces as a solution to detect gait events without a load cell. A floating body dynamic model is obtained with constraints at the center of pressure representing foot-ground interaction. Constraint forces are derived to estimate ground reaction forces and subsequently timing of gait events. A treadmill experiment is conducted with a powered knee-ankle prosthesis used by an able-bodied subject walking at various speeds and slopes. Results show accurate gait event timing, with pooled data showing heel strike detection lagging by only 6.7 ± 7.2 ms and toe off detection leading by 30.4 ± 11.0 ms compared to values obtained from the load cell. These results establish proof of concept for predicting gait events without a load cell in powered prostheses with proprioceptive actuators.

Unsupervised Gait Event Identification with a Single Wearable Accelerometer and/or Gyroscope: A Comparison of Methods across Running Speeds, Surfaces, and Foot Strike Patterns.

We evaluated 18 methods capable of identifying initial contact (IC) and terminal contact (TC) gait events during human running using data from a single wearable sensor on the shank or sacrum. We adapted or created code to automatically execute each method, then applied it to identify gait events from 74 runners across different foot strike angles, surfaces, and speeds. To quantify error, estimated gait events were compared to ground truth events from a time-synchronized force plate. Based on our findings, to identify gait events with a wearable on the shank, we recommend the Purcell or Fadillioglu method for IC (biases +17.4 and -24.3 ms; LOAs -96.8 to +131.6 and -137.0 to +88.4 ms) and the Purcell method for TC (bias +3.5 ms; LOAs -143.9 to +150.9 ms). To identify gait events with a wearable on the sacrum, we recommend the Auvinet or Reenalda method for IC (biases -30.4 and +29.0 ms; LOAs -149.2 to +88.5 and -83.3 to +141.3 ms) and the Auvinet method for TC (bias -2.8 ms; LOAs -152.7 to +147.2 ms). Finally, to identify the foot in contact with the ground when using a wearable on the sacrum, we recommend the Lee method (81.9% accuracy).

Lack of visual information alters lower limb motor coordination to control center of mass trajectory during walking

Vision, as queen of the senses, plays a critical role in guiding locomotion. Little is known about the effects of vision on gait coordination in terms of variability. The uncontrolled manifold (UCM) approach offers a window to the structure of motor variability that has been difficult to obtain from the traditional correlation analysis. In this study, we used the UCM analysis to quantify how the lower limb motion is coordinated to control the center of mass (COM) while walking under different visual conditions. We also probed how synergy strength evolved along the stance phase. Ten healthy participants walked on the treadmill with and without visual information. Leg joint angle variance with respect to the whole-body COM was partitioned into good (i.e., the one that kept the COM) and bad (i.e., the one that changed the COM) variances. We observed that after vision was eliminated, both variances increased throughout the stance phase while the strength of the synergy (the normalized difference between the two variances) decreased significantly and even reduced to zero at heel contact. Thus, walking with restricted vision alters the strength of the kinematic synergy to control COM in the plane of progression. We also found that the strength of this synergy varied across different walking phases and gait events in both visual conditions. We concluded that the UCM analysis can quantify altered coordination of COM when vision is blocked and sheds insights on the role of vision in the synergistic control of locomotion.

Validity of artificial intelligence-based markerless motion capture system for clinical gait analysis: Spatiotemporal results in healthy adults and adults with Parkinson’s disease

Markerless motion capture methods are continuously in development to target limitations encountered in marker-, sensor-, or depth-based systems. Previous evaluation of the KinaTrax markerless system was limited by differences in model definitions, gait event methods, and a homogenous subject sample. The purpose of this work was to evaluate the accuracy of spatiotemporal parameters in the markerless system with an updated markerless model, coordinate- and velocity-based gait events, and subjects representing young adult, older adult, and Parkinson’s disease groups. Fifty-seven subjects and 216 trials were included in this analysis. Interclass correlation coefficients showed excellent agreement between the markerless system and a marker-based reference system for all spatial parameters. Temporal variables were similar, except swing time which showed good agreement. Concordance correlation coefficients were similar with all but swing time showing moderate to almost perfect concordance. Bland-Altman bias and limits of agreement (LOA) were small and improved from previous evaluations. Parameters showed similar agreement across coordinate- and velocity-based gait methods with the latter showing generally smaller LOAs. Improvements in spatiotemporal parameters in the present evaluation was due to inclusion of keypoints at the calcanei in the markerless model. Consistency in the calcanei keypoints relative to heel marker placements may improve results further. Similar to previous work, LOAs are within boundaries to detect differences in clinical groups. Results support the use of the markerless system for estimation of spatiotemporal parameters across age and clinical groups, but caution should be taken when generalizing findings due to remaining error in kinematic gait event methods.

Automated Implementation of the Edinburgh Visual Gait Score (EVGS) Using OpenPose and Handheld Smartphone Video

Recent advancements in computing and artificial intelligence (AI) make it possible to quantitatively evaluate human movement using digital video, thereby opening the possibility of more accessible gait analysis. The Edinburgh Visual Gait Score (EVGS) is an effective tool for observational gait analysis, but human scoring of videos can take over 20 min and requires experienced observers. This research developed an algorithmic implementation of the EVGS from handheld smartphone video to enable automatic scoring. Participant walking was video recorded at 60 Hz using a smartphone, and body keypoints were identified using the OpenPose BODY25 pose estimation model. An algorithm was developed to identify foot events and strides, and EVGS parameters were determined at relevant gait events. Stride detection was accurate within two to five frames. The level of agreement between the algorithmic and human reviewer EVGS results was strong for 14 of 17 parameters, and the algorithmic EVGS results were highly correlated (r > 0.80, “r” represents the Pearson correlation coefficient) to the ground truth values for 8 of the 17 parameters. This approach could make gait analysis more accessible and cost-effective, particularly in areas without gait assessment expertise. These findings pave the way for future studies to explore the use of smartphone video and AI algorithms in remote gait analysis.

Classification of gait phases based on a machine learning approach using muscle synergy.

The accurate detection of the gait phase is crucial for monitoring and diagnosing neurological and musculoskeletal disorders and for the precise control of lower limb assistive devices. In studying locomotion mode identification and rehabilitation of neurological disorders, the concept of modular organization, which involves the co-activation of muscle groups to generate various motor behaviors, has proven to be useful. This study aimed to investigate whether muscle synergy features could provide a more accurate and robust classification of gait events compared to traditional features such as time-domain and wavelet features. For this purpose, eight healthy individuals participated in this study, and wireless electromyography sensors were attached to four muscles in each lower extremity to measure electromyography (EMG) signals during walking. EMG signals were segmented and labeled as 2-class (stance and swing) and 3-class (weight acceptance, single limb support, and limb advancement) gait phases. Non-negative matrix factorization (NNMF) was used to identify specific muscle groups that contribute to gait and to provide an analysis of the functional organization of the movement system. Gait phases were classified using four different machine learning algorithms: decision tree (DT), k-nearest neighbors (KNN), support vector machine (SVM), and neural network (NN). The results showed that the muscle synergy features had a better classification accuracy than the other EMG features. This finding supported the hypothesis that muscle synergy enables accurate gait phase classification. Overall, the study presents a novel approach to gait analysis and highlights the potential of muscle synergy as a tool for gait phase detection.

Estimation of Gait Subphase Time Parameters Based on a Human Electrostatic Field Detection System

The parameters of gait phases obtained by detecting gait events can provide a robust basis for the pathological diagnosis and rehabilitation of patients with lower limb disorders. In this paper, a method is proposed to divide the inner-stance phase and inner-swing phase by detecting the gait events of heel-off (HO) and swing to the highest position (SHP) in the stepping electrostatic gait signal (EGS) and to estimate the subphase time parameters. The gait events are detected by using the local extreme points of the integrated signal of the EGS. Four subphase temporal parameters are obtained after combining the local extrema of the EGS representing the initial contact (IC) and toe-off (TO) for the division of the inner-stance and inner-swing phases. Compared with the motion capture system (VICON), the detection accuracy of the proposed method reaches 98.5%, and the measurement error is within the range of ±60 ms. Pearson’s coefficient ‘r’ was used to verify the correlation between the four gait subphases estimated by the two methods, and excellent agreement was obtained (r > 0.95, p < 0.05). Most of the measurement results of the four subphase temporal gait parameters are within the 95% confidence interval of the Bland–Altman analysis chart. The repeatability of the proposed method was calculated through intraclass correlation coefficients (ICC) and showed good test-retest reliability (ICC > 0.85, p < 0.01). This method detects gait events and estimates subphase duration with high accuracy and can provide a foundation for the gait analysis of EGSs.

Enhancing Mobility in Persons with Footdrop in Multiple Sclerosis through Functional Electrical Stimulation

Background Footdrop is a significant problem in individuals living with multiple sclerosis (MS), leading to falls and reduced quality of life. Functional electrical stimulation (FES) is a common treatment for footdrop. Technological advances are leading to the development of the next generation of FES devices, such as the non-invasive EvoWalk (Evolution Devices, Inc). Prior to widespread use the accuracy of the device's gait detection needs to be validated. Aim To validate EvoWalk's gait analysis accuracy based on ground truths from a pressure-based walkway gait analysis system and examine the improvement in gait with FES in pwMS. Method Individuals living with MS, unilateral footdrop and capable of walking 10m with or without an assistive device were eligible. Participants performed a series of walking and stair-climbing tasks while wearing the EvoWalk device with and without FES stimulation active on the affected leg. Data was simultaneously recorded on the EvoWalk device, and the Zeno pressure-based walkway gait analysis system. Results Data from two pwMS with unilateral footdrop and one healthy control was analyzed. Compared to the pressure-based walkway system, EvoWalk yielded an average heel-strike and toe-off onset detection differences of 32.7ms (SE=15.3, 95% CI = [2.72,62.7]) and 59.1ms (SE=36.4, 95% CI = [0,130.4]), respectively. Also, in one pwMS, swing-time improved by 11.83% when the stimulation was active. Due to technical issues, we were unable to compare the stimulation results for the other pwMS and stimulation was inactive for the control participant. Discussion The wearable FES device tested here was accurate in detecting gait events and has some benefit to gait parameters. Further research concerning the device is warranted. Footdrop is a significant problem in individuals living with multiple sclerosis (MS), leading to falls and reduced quality of life. Functional electrical stimulation (FES) is a common treatment for footdrop. Technological advances are leading to the development of the next generation of FES devices, such as the non-invasive EvoWalk (Evolution Devices, Inc). Prior to widespread use the accuracy of the device's gait detection needs to be validated. To validate EvoWalk's gait analysis accuracy based on ground truths from a pressure-based walkway gait analysis system and examine the improvement in gait with FES in pwMS. Individuals living with MS, unilateral footdrop and capable of walking 10m with or without an assistive device were eligible. Participants performed a series of walking and stair-climbing tasks while wearing the EvoWalk device with and without FES stimulation active on the affected leg. Data was simultaneously recorded on the EvoWalk device, and the Zeno pressure-based walkway gait analysis system. Data from two pwMS with unilateral footdrop and one healthy control was analyzed. Compared to the pressure-based walkway system, EvoWalk yielded an average heel-strike and toe-off onset detection differences of 32.7ms (SE=15.3, 95% CI = [2.72,62.7]) and 59.1ms (SE=36.4, 95% CI = [0,130.4]), respectively. Also, in one pwMS, swing-time improved by 11.83% when the stimulation was active. Due to technical issues, we were unable to compare the stimulation results for the other pwMS and stimulation was inactive for the control participant. The wearable FES device tested here was accurate in detecting gait events and has some benefit to gait parameters. Further research concerning the device is warranted.

Simplified Markerless Stride Detection Pipeline (sMaSDP) for Surface EMG Segmentation.

To diagnose mobility impairments and select appropriate physiotherapy, gait assessment studies are often recommended. These studies are usually conducted in confined clinical settings, which may feel foreign to a subject and affect their motivation, coordination, and overall mobility. Conducting gait studies in unconstrained natural settings instead, such as the subject's Activities of Daily Life (ADL), could provide a more accurate assessment. To appropriately diagnose gait deficiencies, muscle activity should be recorded in parallel with typical kinematic studies. To achieve this, Electromyography (EMG) and kinematic are collected synchronously. Our protocol sMaSDP introduces a simplified markerless gait event detection pipeline for the segmentation of EMG signals via Inertial Measurement Unit (IMU) data, based on a publicly available dataset. This methodology intends to provide a simple, detailed sequence of processing steps for gait event detection via IMU and EMG, and serves as tutorial for beginners in unconstrained gait assessment studies. In an unconstrained gait experiment, 10 healthy subjects walk through a course designed to mimic everyday walking, with their kinematic and EMG data recorded, for a total of 20 trials. Five different walking modalities, such as level walking, ramp up/down, and staircase up/down are included. By segmenting and filtering the data, we generate an algorithm that detects heel-strike events, using a single IMU, and isolates EMG activity of gait cycles. Applicable to different datasets, sMaSDP was tested in healthy gait and gait data of Parkinson's Disease (PD) patients. Using sMaSDP, we extracted muscle activity in healthy walking and identified heel-strike events in PD patient data. The algorithm parameters, such as expected velocity and cadence, are adjustable and can further improve the detection accuracy, and our emphasis on the wearable technologies makes this solution ideal for ADL gait studies.

Dealing with the heterogeneous presentations of freezing of gait: how reliable are the freezing index and heart rate for freezing detection?

BackgroundFreezing of gait (FOG) is an unpredictable gait arrest that hampers the lives of 40% of people with Parkinson’s disease. Because the symptom is heterogeneous in phenotypical presentation (it can present as trembling/shuffling, or akinesia) and manifests during various circumstances (it can be triggered by e.g. turning, passing doors, and dual-tasking), it is particularly difficult to detect with motion sensors. The freezing index (FI) is one of the most frequently used accelerometer-based methods for FOG detection. However, it might not adequately distinguish FOG from voluntary stops, certainly for the akinetic type of FOG. Interestingly, a previous study showed that heart rate signals could distinguish FOG from stopping and turning movements. This study aimed to investigate for which phenotypes and evoking circumstances the FI and heart rate might provide reliable signals for FOG detection.MethodsSixteen people with Parkinson’s disease and daily freezing completed a gait trajectory designed to provoke FOG including turns, narrow passages, starting, and stopping, with and without a cognitive or motor dual-task. We compared the FI and heart rate of 378 FOG events to baseline levels, and to stopping and normal gait events (i.e. turns and narrow passages without FOG) using mixed-effects models. We specifically evaluated the influence of different types of FOG (trembling vs akinesia) and triggering situations (turning vs narrow passages; no dual-task vs cognitive dual-task vs motor dual-task) on both outcome measures.ResultsThe FI increased significantly during trembling and akinetic FOG, but increased similarly during stopping and was therefore not significantly different from FOG. In contrast, heart rate change during FOG was for all types and during all triggering situations statistically different from stopping, but not from normal gait events.ConclusionWhen the power in the locomotion band (0.5–3 Hz) decreases, the FI increases and is unable to specify whether a stop is voluntary or involuntary (i.e. trembling or akinetic FOG). In contrast, the heart rate can reveal whether there is the intention to move, thus distinguishing FOG from stopping. We suggest that the combination of a motion sensor and a heart rate monitor may be promising for future FOG detection.

Feature Selection using Random Forest Classifier for Foot Strike Event Detection in Toe Walkers

Automated Gait event identification of Foot Strike (FS) and Foot Off (FO) in pathological gait data, can be time saving in comparison to conventional manual annotations done currently. Identification of FS and FO allows breaking walking trials into gait cycles and hence aids in comparison of gait parameters like joint angles, forces and moments across gait cycles. Automated Gait Event Detection is also useful in development of wearable sensor devices and robotic systems that assist gait. Researchers have proposed several automatic gait event detection algorithms based on kinematic parameters and systematic study of the literature suggests specific parameters to have higher contribution in identification of FS event in all common pathological gait patterns. We used Random Forest Classifier Feature selection technique to identify high contributing features in FS event in toe walking pediatric pathological gait dataset and the results suggest high similarity in selected features by the machine learning technique with those suggested by popular event detection algorithms based on kinematic parameters for pathological gait. Hence we conclude that RFC feature selection is suitable for feature selection in toe walkers gait dataset for event detection purpose. Keyword : Feature selection, foot off, foot strike, pathological gait.

Co-simulation of human digital twins and wearable inertial sensors to analyse gait event estimation.

We propose a co-simulation framework comprising biomechanical human body models and wearable inertial sensor models to analyse gait events dynamically, depending on inertial sensor type, sensor positioning, and processing algorithms. A total of 960 inertial sensors were virtually attached to the lower extremities of a validated biomechanical model and shoe model. Walking of hemiparetic patients was simulated using motion capture data (kinematic simulation). Accelerations and angular velocities were synthesised according to the inertial sensor models. A comprehensive error analysis of detected gait events versus reference gait events of each simulated sensor position across all segments was performed. For gait event detection, we considered 1-, 2-, and 4-phase gait models. Results of hemiparetic patients showed superior gait event estimation performance for a sensor fusion of angular velocity and acceleration data with lower nMAEs (9%) across all sensor positions compared to error estimation with acceleration data only. Depending on algorithm choice and parameterisation, gait event detection performance increased up to 65%. Our results suggest that user personalisation of IMU placement should be pursued as a first priority for gait phase detection, while sensor position variation may be a secondary adaptation target. When comparing rotatory and translatory error components per body segment, larger interquartile ranges of rotatory errors were observed for all phase models i.e., repositioning the sensor around the body segment axis was more harmful than along the limb axis for gait phase detection. The proposed co-simulation framework is suitable for evaluating different sensor modalities, as well as gait event detection algorithms for different gait phase models. The results of our analysis open a new path for utilising biomechanical human digital twins in wearable system design and performance estimation before physical device prototypes are deployed.

Robust Gait Event Detection Based on the Kinematic Characteristics of a Single Lower Extremity

Robust Gait Event Detection Based on the Kinematic Characteristics of a Single Lower Extremity

Vibrations on mastoid process alter the gait characteristics during walking on different inclines.

Eighty-eight percent of the persons with bilateral vestibular dysfunction have reported at least one fall within the past 5 years. The apparent alternations due to the bilateral vestibular dysfunctions (BVD) are the gait characteristics, such as slower walking speed, prolonged stance phase, and shorter step length. Unexpectedly, due to the prevalence of this BVD being relatively low, attention is not obtained as same as in other vestibular disorders. Moreover, how does walking on different inclines, part of daily activities, alter the gait characteristics under the unreliable bilateral vestibular systems? Previous studies used vibration-based stimulations (VS) as a perturbation to understand the postural control during walking while the bilateral vestibular systems were perturbed. Therefore, this study attempted to extend the knowledge to understand the alternations in spatial-temporal gait characteristics under perturbed bilateral vestibular systems while walking on different inclines. Nineteen healthy young adults participated in this study. Eight walking conditions were randomly assigned to each participant: 0%, 3%, 6%, and 9% grade of inclines with/without VS. The preferred walking speed was used for gait analysis. The dependent variables were stance time, double support time, step length, step time, step width, foot clearance, and respective variabilities. All dependent variables were defined by two critical gait events: heel-strike and toe-off. Pre-Hoc paired comparisons with Bonferroni corrections were used to prioritize the dependent variables. A two-way repeated measure was used to investigate the effect of VS and the effect of inclines on the selected dependent variables from Pre-Hoc analysis. Post-Hoc comparisons were also corrected by the Bonferroni method. The step length, step time, foot clearance, and foot clearance variability were selected by the Pre-Hoc analysis because the corrected paired t-test demonstrated a significant VS effect (p < 0.05) on these gait parameters at least one of four inclines. The significant interaction between the effect of VS and the effect of inclines was found in step length (p = 0.005), step time (p = 0.028), and foot clearance variability (p = 0.003). The results revealed that implementing a VS increased step length and step time when walking on 0%, 3%, and 9% of grade inclines. In particular, the foot clearance variability was found when walking on 9% of grade inclines. The observations in the current study suggested that VS increased the step length, step time, foot clearance, and foot clearance variability while walking on inclines. These results suggested that these gait parameters might be promising targets for future clinical investigations in patients with BVD while walking on different inclines. Importantly, the increases in spatial-temporal gait performance under bilateral VS might be an indicator of gait improvement while walking on different inclines.

Development of a new low-cost computer vision system for human gait analysis: A case study.

Today, human gait analysis is commonly used for clinical diagnosis, rehabilitation and performance improvement in sports. However, although previous research works in the literature address the use of motion capture systems by means of optoelectronic sensors, Inertial Measurement Units (IMUs) and depth cameras, few of them discuss their conception, guidelines and algorithms for measuring and calculating gait metrics. Moreover, commercially available motion capture systems, although efficient, are cost restrictive for most of the low-income institutions. In this research work, a new computer vision-based system (CVS) for gait analysis is developed and proposed. The aim is to close the gap found in the literature about the design and development of such systems by providing the requirements, considerations, algorithms and methodologies used to develop a gait analysis system with acceptable precision and accuracy, and at low cost. For this purpose, a linear computer vision method based on the non-homogeneous solution of the calibration matrix was used. The spatio-temporal and angular gait parameters were implemented in the proposed system, and compared with those reported in the literature. The denoising of the spatial gait trajectories and the strategies to detect gait events, are also presented and discussed. The results have shown that the proposed system is satisfactory for human gait analysis in terms of precision, computational performance and low cost.

Semi-supervised approach to identify steps, shoes, and gender of older adults under semi-naturalistic conditions using a waist-worn inertial sensor.

An emerging source of information to recognize individuals' characteristics are the walking pattern-related parameters. The elderly can be one of the populations that can benefit most from recognition-based applications, which may help to increase their possibilities of living independently at home. Approaches have been mostly focused on gait events' identification or assessment; nonetheless, such information can also be used to obtain seniors' characteristics that depend on physiological or environmental factors. These factors can be useful to provide a customized assistance based on contextual information. In this paper, we propose a method focused on seniors, to detect steps, and to recognize gender and type of shoes by using only the initial foot contact (IC) data obtained from inertial sensors during semi-controlled walking. Data were collected from 20 older adults who walked at self-speed in a natural environment. The method consists of first clustering the IC using k-means; then, a trained recurrent neural network recognizes gender, type of shoes, and the step phases (IC and other phases); to finally conduct step detection (SD) using a ruled-based method. The method recognizes gender and the type of shoes with an accuracy of 93% and 83.07%, respectively, whereas there were not misrecognitions of the step phases. SD achieved a mean absolute percentage error equal to . The good results show that the method is appropriate for users' characteristics recognition applications without depending on assumptions based on individualities. Likewise, the method can be useful to monitor physical activity or systems aimed to keep safe older adults.