Walking In Subjects Research Articles

Multi-branch deep learning neural network prediction model for the development of angular biosensors based on sEMG.

Human gait motion intention recognition is very important for the lower extremity exoskeleton robot to accurately synchronize and respond to the user's natural motion. And motion intention recognition is generally performed through sEMG. Deep learning neural networks perform well in dealing with high-dimensional data and nonlinear relationships such as sEMG, but different deep learning neural networks have their own advantages in dealing with different types of data. Therefore, a multi-branch deep learning neural network, which enables different neural networks to process different feature items, could achieve more accurate and efficient motion intention recognition. The purpose of this study is to 1) Establish a multi-branch deep learning neural network model to achieve accurate gait recognition and effective estimation of joint angles. 2) Quantify the performance of the multi-branch deep learning neural network model in gait recognition and joint angle prediction using sEMG. This study involved the collection of sEMG and plantar pressure data during walking in human subjects. Firstly, the collected signals are filtered and denoised to ensure the quality and reliability of the data. Calculate the time domain features and the frequency domain features to capture the key information of gait. Then, using the sensitivity difference of different structural neural networks to different feature data, a multi-branch deep learning neural network model is developed, in which the extracted features are used as the input of the model. The output of the model includes gait cycle and joint angle, so as to realize the accurate recognition of human gait and the effective estimation of joint angle. The results show that the proposed method has high accuracy in identifying human gait and estimating joint angles. The multi-branch neural network model successfully integrates time-domain and frequency-domain features and provides reliable prediction of gait cycle and joint angle. The highest accuracy of gait recognition is 95.42%, the lowest is 90.11%, and the average is 92.16%. The average error of joint angle estimation is 3.19. This study designed a human walking gait recognition and joint angle prediction model to achieve accurate human lower limb motion intention recognition.The model can be integrated into the sEMG sensor to design a angular biosensors, which can predict the human joint angle in real time.

Lattice random walk dynamics with stochastic resetting in heterogeneous space

Abstract We examine the diffusive dynamics of a lattice random walk subject to resetting in a one-dimensional spatially heterogeneous environment composed of two media separated by an interface. At random times the walker may reset its position to the interface, but only when in the left medium. In addition the spatial heterogeneity results from having unequal diffusivities and biases in the two media. We construct the Master equation for the dynamics of the walker occupation probability in unbounded space, solve it exactly in terms of generating functions, and analyse the dynamics of the first and second moment. Making use of the closed form solution in the unbounded case, we build the analytic solution of the Master equation in finite and semi-infinite domains. By bounding the space on the right with a reflecting boundary we study the first-passage dynamics to a single fully absorbing target placed in the left medium away from the interface. As reset strongly increases the time to reach the target, we find that the first-passage dynamics enter the motion-limited regime even for relative small resetting probability. We also identify a surprising non-monotonic dependence of the first-passage probability mode as a function of the bias. By deriving an analytic expression for the mean first-passage time, we show when its value is independent of the diffusivity and bias in the left medium, uncovering another example of the so-called mean disorder indifference phenomenon.

Smartphone IMU Sensors for Human Identification through Hip Joint Angle Analysis.

Gait monitoring using hip joint angles offers a promising approach for person identification, leveraging the capabilities of smartphone inertial measurement units (IMUs). This study investigates the use of smartphone IMUs to extract hip joint angles for distinguishing individuals based on their gait patterns. The data were collected from 10 healthy subjects (8 males, 2 females) walking on a treadmill at 4 km/h for 10 min. A sensor fusion technique that combined accelerometer, gyroscope, and magnetometer data was used to derive meaningful hip joint angles. We employed various machine learning algorithms within the WEKA environment to classify subjects based on their hip joint pattern and achieved a classification accuracy of 88.9%. Our findings demonstrate the feasibility of using hip joint angles for person identification, providing a baseline for future research in gait analysis for biometric applications. This work underscores the potential of smartphone-based gait analysis in personal identification systems.

A study on street walkability for older adults with different mobility abilities combining street view image recognition and deep learning - The case of Chengxianjie Community in Nanjing (China)

China is on the brink of transitioning into an aged society, resulting in a growing demand for an age-friendly street-built environment. However, previous research has paid limited attention to the differentiated walking needs of older adults. To address this gap, this study investigated the relationship between street-built environments and the subjective perception of older adults with different physical capabilities, focusing on safety, comfort, and interest. The older adults were classified into three types based on their physical mobility abilities. The TrueSkill algorithm was used to develop an online image selection website to obtain perception scores for sampled pictures from these three types of older adults. Image segmentation and deep learning were combined to extract indices of street view factors, and machine learning was used to train a scoring prediction model for all streetscape pictures of the area. The study found differences in the subjective perception among all three types of older adults, namely independent elderly (A), mediated-assisted elderly (B), and dependent elderly (C). Type A older adults might be attracted to factors related to the interest of walking despite their negative impact on safety and comfort; Type B older adults were more concerned about street conditions for safety and comfort. Type C older adults were prone to the convenience of barrier-free access and visibility. This study contributes to the study of walkability by providing a research framework for the subjective walking perceptions of older adults with different physical capabilities. Additionally, the visualized walkability map can serve as a reference for architects and urban designers, further strengthening the development of age-friendly communities with the aid of human-centric computational analysis, evaluation, and design.

Effectiveness of forward and retro walking in subjects with osteoarthritis of knee: A comparative study

Effectiveness of forward and retro walking in subjects with osteoarthritis of knee: A comparative study

Neural network-based estimation of lower limb joint kinematics: A minimally intrusive approach for gait analysis

The establishment of a quantitative gait analysis system holds paramount importance, particularly in the context of functional rehabilitation of the lower limbs. Clinicians emphasize the imperative for sensors to be portable, compact, integrated, and non-intrusive, crucial characteristics in the rehabilitation field to facilitate their use and ensure optimal integration into care protocols. This study investigates an innovative approach aimed at reducing the reliance on body-fixed sensors by harnessing their data within a neural network, thus concentrating on the joint kinematics of the lower limbs. The primary objective is to estimate the flexion-extension angles of the hip, knee, and ankle during walking, utilizing data collected by two sensors positioned on the subject's legs. Initially, the neural network undergoes training with calculated data (leg tilt angles and angular velocities) sourced from the OpenSim database, followed by further refinement with experimental data obtained from a subject walking on a treadmill, wherein leg tilt angles and angular velocities are measured. The significance of this research is underscored by the demonstrated capability, through conducted tests, of the implemented networks to efficiently fuse data from a minimal set of sensors. Consequently, the proposed approach emerges as both practical and minimally intrusive, facilitating a robust evaluation of gait kinematic parameters.

Investigation of joint contact forces during walking in the subjects with toe in gait due to increasing in femoral head anteversion angle.

Toe-in gait is a pathology in which the child walks and turns the foot inward instead of pointing straight ahead. The alignment of the lower limb structure changes in this disease, increasing the incidence of knee and hip osteoarthritis. This study aimed to determine the kinematic and joint loading in subjects walking with a toe-in gait pattern. This study selected two groups of subjects: normal subjects and those with toe-in gait due to an increased femoral head anteversion angle (each group consisted of 15 subjects). A Qualisys motion analysis system and a Kistler force plate were used to record the motions and forces applied to the leg while walking. OpenSim software (version 3.3) was used to analyze the range of motion, moments, muscle forces, and joint contact forces in both groups of subjects. The mean values of stride length for normal subjects (1.1 ± 0.141 m) and those with toe-in gait (0.94 ± 0.183 m) differed significantly. The mediolateral component of the ground reaction force decreased significantly in the toe-in gait group compared to normal subjects (p-value = 0.05). The peak force of most of the hip joint muscles increased significantly in those with toe-in gait compared to normal subjects (p-value < 0.05). The results of this study showed that those with toe-in gait, due to an increase in femoral head anteversion angle, only had a change in rotation of the pelvic and hip joint. There was no significant difference between walking speed and most ground reaction force components between normal subjects and those with toe-in gait. As the peaks of most of the hip joint muscles increased significantly in the toe-in gait group, this increased joint contact forces (especially the anteroposterior component of the hip joint and the mediolateral component of the knee joint), which may ultimately increase the incidence of hip and knee joint osteoarthritis.

Gait Variability as a Potential Motor Marker of Cerebellar Disease-Relationship between Variability of Stride, Arm Swing and Trunk Movements, and Walking Speed.

Excessive stride variability is a characteristic feature of cerebellar ataxias, even in pre-ataxic or prodromal disease stages. This study explores the relation of variability of arm swing and trunk deflection in relationship to stride length and gait speed in previously described cohorts of cerebellar disease and healthy elderly: we examined 10 patients with spinocerebellar ataxia type 14 (SCA), 12 patients with essential tremor (ET), and 67 healthy elderly (HE). Using inertial sensors, recordings of gait performance were conducted at different subjective walking speeds to delineate gait parameters and respective coefficients of variability (CoV). Comparisons across cohorts and walking speed categories revealed slower stride velocities in SCA and ET patients compared to HE, which was paralleled by reduced arm swing range of motion (RoM), peak velocity, and increased CoV of stride length, while no group differences were found for trunk deflections and their variability. Larger arm swing RoM, peak velocity, and stride length were predicted by higher gait velocity in all cohorts. Lower gait velocity predicted higher CoV values of trunk sagittal and horizontal deflections, as well as arm swing and stride length in ET and SCA patients, but not in HE. These findings highlight the role of arm movements in ataxic gait and the impact of gait velocity on variability, which are essential for defining disease manifestation and disease-related changes in longitudinal observations.

Full-body kinematics and head stabilisation strategies during walking in patients with chronic unilateral and bilateral vestibulopathy

Chronic imbalance is a frequent and limiting symptom of patients with chronic unilateral and bilateral vestibulopathy. A full-body kinematic analysis of the movement of patients with vestibulopathy would provide a better understanding of the impact of the pathology on dynamic tasks such as walking. Therefore, this study aimed to investigate the global body movement during walking, its variability (assessed with the GaitSD), and the strategies to stabilise the head (assessed with the head Anchoring Index). The full-body motion capture data of 10 patients with bilateral vestibulopathy (BV), 10 patients with unilateral vestibulopathy (UV), and 10 healthy subjects (HS) walking at several speeds (slow, comfortable, and fast) were analysed in this prospective cohort study. We observed only a few significant differences between groups in parts of the gait cycle (shoulder abduction–adduction, pelvis rotation, and hip flexion–extension) during the analysis of kinematic curves. Only BV patients had significantly higher gait variability (GaitSD) for all three walking speeds. Head stabilisation strategies depended on the plan of motion and walking speed condition, but BV and UV patients tended to stabilise their head in relation to the trunk and HS tended to stabilise their head in space. These results suggest that GaitSD could be a relevant biomarker of chronic instability in BV and that the head Anchoring Index tends to confirm clinical observations of abnormal head-trunk dynamics in patients with vestibulopathy while walking.

Alterations in Step Width and Reaction Times in Walking Subjects Exposed to Mediolateral Foot-Transmitted Vibration

This study explores how low-frequency foot-transmitted vibration (FTV) affects both gait parameters and cognitive performance. Twenty healthy male participants experienced harmonic mediolateral FTV (1.25 Hz, 1 m/s2) while either standing or walking on a treadmill. We assessed participants’ reaction times to visual stimuli using a psychomotor vigilance task (PVT) test under five conditions, including (i) baseline (standing still without vibration), (ii) vibration (standing still with vibration), (iii) walking (walking without vibration), (iv) walking with vibration, and (v) post-test (standing still without vibration after the tests). Additionally, the step width (SW) was measured with a camera system in conditions (iii) and (iv), i.e., when participants were walking with and without vibration and during PVT execution. The results showed that the average vigilance decreased, and the step width increased while walking and/or with vibration exposure. These findings suggest a potential connection between decreased vigilance, increased step width, and the need for enhanced stability, focusing on balance maintenance and a wider base of support. Implications for future standard revisions are presented and discussed.

The Effect of Postural Restoration Institute Training on Selected Kinematic Variables of Lower Limbs and Pelvis During Walking in Middle-Aged Men With Nonspecific Chronic Low Back Pain: A Prospective Observational Study

Background and Aims Concerning the effectiveness of training on people with chronic low back pain, the present study aimed to evaluate the effect of postural restoration institute (PRI) training on lower limb kinematics during walking in middle-aged subjects with discogenic low back pain. Methods In this prospective observational quasi-experimental study, 42 people participated in three groups: people without low back pain (healthy group, n=14) and people with low back pain in two groups (intervention group, n=14; and control group, n=14). Kinematic data were recorded by 7 Qualisys cameras with a frequency of 100 Hz in the pretest and post-test. Analysis of variance with repeated measures was used in SPSS software version 23 at a significance level 0.05. Results The results showed that people with low back pain in the intervention and control groups walk with a slower velocity, lower cadence, and shorter step length than healthy individuals for both right and left limbs. However, after the PRI training, the intervention group showed a significant increase in those three variables. Furthermore, there were significant differences between the healthy group and the two groups of intervention and control in the range of motion (ROM) for the pelvis in the sagittal and frontal planes, the right hip joint in the horizontal plane, the right ankle in the frontal plane, the left hip joint in the sagittal and horizontal planes as well as the left ankle in the sagittal plane. After applying PRI, the differences between the healthy and the intervention groups regarding the pelvis ROM on the sagittal plane, the right ankle ROM on the sagittal plane, the left hip joint ROM on the frontal plane, and the left knee ROM on the sagittal plane were minimized and became insignificant. Conclusion The present study’s findings confirmed the effectiveness of using PRI training in improving biomechanical indicators, such as the range of joint movements and increasing the velocity and step length of walking, to treat and manage the performance of people with discogenic chronic low back pain.

Identifying the interrelations between subjective walkability factors and walking behaviour: A case study in Jeddah, Saudi Arabia

Promoting pedestrian-friendly environments has become a subject of great interest worldwide, as it crosses a wide range of scientific fields. Numerous factors have been proposed to measure the level of walkability, but no consensus has been reached on the factors to consider when measuring walkability and the effects of these factors on walking behaviour. Thus, this study aimed to identify objective and subjective walkability factors and their relation to current walking behaviour. The findings were expected to contribute to the design and planning of walkable cities and neighbourhoods. The study designed a theoretical framework stemming from social psychology and urban transport geography to elucidate the influence of walkability factors on walking behaviour. Based on this framework, a survey was conducted among 556 participants. Results of the multivariate correlation analyses and binary logistic regression revealed views that differed from those reported in the literature for other contexts. The comparative analysis based on individuals’ characteristics and travel purposes did not detect any strong variation in walking behaviour. Other results were consistent with previous findings on issues of the hierarchy of walking needs. People’s decision to walk in the selected region was not necessarily determined only by the current condition of the physical built environment. Among all the factors studied, factors concerning accessibility, safety in terms of traffic, and comfort were found to be important for determining walking behaviour. Future studies should enhance walkability measurements by examining the interrelations between subjective walkability factors and the physical built environment.

Measurement of Physical Activity Among Hospitalized Older Adults Living With Dementia.

The purpose of this secondary data analysis was to describe physical activity and the factors associated with physical activity among older adults living with dementia on medical units in acute care settings. Measures included accelerometry data from the MotionWatch 8, behavioral and psychological symptoms associated with dementia, use of psychotropic medications, subjective reports of activities of daily living and other types of physical activity (e.g., walking to the bathroom, participating in therapy), delirium severity, and medications. The majority of the 204 participants were White (70%) and female (62%), with a mean age of 83 years. Over 24 hours of assessment, participants engaged in 15 ( SD = 46) minutes of vigorous activity, 43 ( SD = 54) minutes of moderate activity, 2 hours 50 ( SD = 2) minutes of low-level activity, and 20 ( SD = 3) hours of sedentary activity. Subjective walking activities, toileting, evidence of disinhibition, delirium severity, agitation, and use of psychotropic medications were associated with increased physical activity based on the MotionWatch 8. The findings provide information for rehabilitation nurses regarding factors associated with physical activity among patients with dementia admitted to acute care settings as well as some of the challenges associated with measurement of physical activity. Future research needs to continue to explore the impact of behavioral symptoms associated with dementia on physical activity and increase participation in activities that are functionally relevant.

Speed-dependent modulations of asymmetric center of body mass trajectory in the gait of above-knee amputee subjects.

How to achieve stable locomotion while overcoming various instabilities is an ongoing research topic. One essential factor for achieving a stable gait is controlling the center of body mass (CoM). The CoM yields more instability in the mediolateral direction. Examining speed-dependent modulations of the CoM trajectories in the frontal plane can provide insight into control policies for achieving stable locomotion. Although these modulations have been studied while assuming symmetric CoM trajectories, this assumption is generally incorrect. For example, amputee subjects demonstrate asymmetric CoM trajectories. Here, we investigated speed-dependent modulations of asymmetric CoM trajectories in above-knee amputee subjects using Fourier series expansion. Despite the asymmetric CoM trajectories in amputee subjects, the framework of Fourier series expansion clarified that amputee subjects showed the same speed-dependent modulations as non-amputee subjects whose CoM trajectories were symmetric. Specifically, CoM trajectories became narrower in the mediolateral direction and broader in the superoinferior direction as walking speed increased. The speed-dependent modulations of CoM trajectories had a functional role in improving dynamic stability, and faster walking speeds provided greater dynamic stability on both prosthetic and non-prosthetic sides. Although the asymmetry of foot contact duration and CoM trajectory decreased as walking speed increased, step width and the asymmetry of dynamic stability between prosthetic and non-prosthetic sides remained constant across the walking speed, which corresponded to the predictions by our framework. These findings could offer a better strategy for achieving stable walking for amputee subjects.

Descriptor: Radar Acquired Data on Subjects Walking on a Treadmill (RADSWT)

Descriptor: Radar Acquired Data on Subjects Walking on a Treadmill (RADSWT)

AFPILD: Acoustic footstep dataset collected using one microphone array and LiDAR sensor for person identification and localization

Empowering the auditory perception-based person identification and localization could enable the service robots to navigate among the crowds in a more socially compliant manner. And it is more user-friendly with fewer privacy concerns compared to the widely studied camera-supported approaches. One way to achieve such auditory person identification and localization is to identify and locate the subject by hearing her/his footstep. As far as we know, although several related acoustic identification benchmark datasets have been made, the dataset that simultaneously concentrates on acoustic identification and localization is absent. Aiming at the shortage of such benchmark datasets, we propose to build the acoustic footstep-based person identification and localization dataset (AFPILD) by unifying the identify and locate tasks for the first time, concerning the clothing and shoe type covariates from the human-aware mobile service robot navigation perspective. Specifically, we record the walking sounds of 40 subjects with a lightweight microphone array in one real unstrained indoor room. For ground truth localization label annotation, we record the point clouds of the walking subjects with a multi-line LiDAR synchronously. The AFPILD contains nearly 10 h of footstep audios (around 62,174 footstep events). To the best of our knowledge, this is the largest and most comprehensive conducted acoustic footstep dataset built for person identification and localization study up to now. We also present the baseline works concerning the identification, localization, and synchronous identify-locate tasks, respectively, to compare various potential approaches on this dataset in the future. Experimental results show that the covariates of clothing and shoe type degrade the footstep identification (around 12% of clothing and 33% of shoe type), while their impacts on the localization are unobvious.

Doing housework and having regular daily routine standing out as factors associate with physical function in the older people.

Nationwide data were used to explore factors associated with physical function in order to identify interventions that could improve and maintain physical function in the older people. The physical function was assessed by gait speed (GS). We selected 2,677 male and 2,668 female older adults (aged ≥60) who could perform the GS test as study subjects. GS was measured by having subjects walk across and back a 10-m course. A gait speed less than 20% that of a reference population (<0.7 m/s) was used as the definition of slow gait speed (SGS). Co-morbidity, polypharmacy, medical expenses, need for care, and hospitalization were used to evaluate health status. A stepwise logistic regression model was used to determine factors associated with SGS. SGS was associated with poorer health status, higher medical cost, lower ranking on the Geriatric Depression Scale (GDS) and decreased Mini-mental State Examination (MMSE). Co-morbidity (OR = 1.81, 1.58-2.07), polypharmacy (OR = 1.47, 1.25-1.74), MMSE <24 (OR = 1.85, 1.54-2.22), and GDS ≥ 11 (OR = 1.40, 1.18-1.65) were associated with SGS. In contrast, doing housework (DHW, OR = 0.43, 0.38-0.49), having a regular daily routine (RDR, OR = 0.64, 0.45-0.91), and current alcohol consumption (OR = 0.74, 0.62-0.90) were inversely associated with SGS. DHW plus having RDR could greatly reduce the risk of SGS (OR = 0.29, 0.19-0.43). Poor physical function is associated with poorer health status in Chinese older people. Maintaining a regular daily routine and doing some housework may be important factors that can help older people preserve their physical function.

Knowledge, attitude and practice of physical exercises among pregnant women attending prenatal care clinics of public health institutions in Hawassa city, Sidama, Ethiopia, in 2021: descriptive cross-sectional study

BackgroundParticipating in physical exercise enhances the physical and mental health of pregnant women. Preventing excessive weight gain, decreasing gestational hypertension, reducing back pain and labor complications are some of the main benefits of physical exercise during pregnancy and childbirth. Scrutinizing factors associated with sedentary life style among women during pregnancy could aid to design effective strategies to tackle the problem. Despite its benefit, little is explored about knowledge, attitude and practice of physical exercise among pregnant women in the study setting.ObjectiveTo assess prevalence of knowledge, attitude and practice of physical exercise among pregnant women who visit antenatal care at public health facilities of Hawassa town, Ethiopia, in 2023.MethodsFacility based survey was conducted from November-December, in 2021. Data were collected using interview administered and structured questionnaire. Data were cleaned, coded and entered using Epi-data 4.6 and exported into SPSS 25 for analysis. Descriptive statistics was done using frequency count, percentage and mean values of variables. Finally, findings are presented using text, tables and charts.ResultsAll of the study subjects completed interview making a response rate of 100% in this study. The mean adequate knowledge score was 42.2%. Positive attitude towards physical exercise during pregnancy was accounted as 63.7% and proportion of good practice of physical exercise was as 35.8%. Regarding practice of exercise, most (95.9%) of the subjects walk, however; only 11(8.9%) women perform pelvic floor exercise were the highest and least practiced physical exercise. Concerning knowledge of exercise, prevent excess weight 72.1% and increasing energy 53.2% were the commonly known benefits of physical exercise. Breathing difficulty (41.3%), chest pain (39.8%) and premature labor (34.0%) were the predominant perceptions of contra-indication of physical exercise during pregnancy.ConclusionsIn conclusion, the proportion of knowledge, attitude and practice of antenatal exercise is found to be sub-optimum in the study area. Therefore, health education should be enhanced about the benefit of physical exercise during pregnancy.

FPGA-Based CNN for Eye Detection in an Iris Recognition at a Distance System

Neural networks are the state-of-the-art solution to image-processing tasks. Some of these neural networks are relatively simple, but the popular convolutional neural networks (CNNs) can consist of hundreds of layers. Unfortunately, the excellent recognition accuracy of CNNs comes at the cost of very high computational complexity, and one of the current challenges is managing the power, delay and physical size limitations of hardware solutions dedicated to accelerating their inference process. In this paper, we describe the embedding of an eye detection system on a Zynq XCZU4EV UltraScale+ multiprocessor system-on-chip (MPSoC). This eye detector is used in the application framework of a remote iris recognition system, which requires high resolution images captured at high speed as input. Given the high rate of eye regions detected per second, it is also important that the detector only provides as output images eyes that are in focus, discarding all those seriously affected by defocus blur. In this proposal, the network will be trained only with correctly focused eye images to assess whether it can differentiate this pattern from that associated with the out-of-focus eye image. Exploiting the neural network’s advantage of being able to work with multi-channel input, the inputs to the CNN will be the grey level image and a high-pass filtered version, typically used to determine whether the iris is in focus or not. The complete system synthetises other cores and implements CNN using the so-called Deep Learning Processor Unit (DPU), the intellectual property (IP) block released by AMD/Xilinx. Compared to previous hardware designs for implementing FPGA-based CNNs, the DPU IP supports extensive deep learning core functions, and developers can leverage DPUs to conveniently accelerate CNN inference. Experimental validation has been successfully addressed in a real-world scenario working with walking subjects, demonstrating that it is possible to detect only eye images that are in focus. This prototype module includes a CMOS digital image sensor that provides 16 Mpixel images, and outputs a stream of detected eyes as 640 × 480 images. The module correctly discards up to 95% of the eyes present in the input images as not being correctly focused.

The Orthotic Effects of Different Functional Electrical Stimulation Protocols on Walking Performance in Individuals with Incomplete Spinal Cord Injury: A Case Series.

Functional electrical stimulation (FES) of paralyzed muscles can facilitate walking after spinal cord injury (SCI). To test the orthotic effects of different FES walking protocols on lower joint kinematics and walking speed. Three adults with incomplete SCI participated in this study. Their lower extremity motor scores and 10-meter walk test results were as follows: subject A: 50, 1.05 m/s, subject B: 44, 0.29 m/s, and subject C: 32, 0.27 m/s. Participants completed four conditions of over-ground walking including no FES and three bilateral FES-walking protocols as follows: multi-muscle stimulation (stimulation of quadriceps and gastrocnemius in the stance phase, and hamstring and tibialis anterior in the swing phase), drop foot (tibialis anterior stimulation), and flexor withdrawal (common peroneal nerve stimulation). The FES system obtained gait phase information from foot switches located under the individuals' heels. Three-dimensional kinematic analysis was undertaken to measure minimum toe clearance (MTC); ankle, knee, and hip range of motion (ROM); stride length; and stride speed. Compared to no-FES walking, MTC increased during drop foot (all subjects), flexor withdrawal (subjects A and B), and multi-muscle stimulation (subjects B and C) protocols. A significant decrease in ankle ROM was seen with drop foot (all subjects), flexor withdrawal (subjects A), and multi-muscle stimulation (subjects A and C) protocols. Hip ROM increased with drop foot (subjects B and C), flexor withdrawal (subject B), and multi-muscle stimulation (subject C) protocols. Three FES walking protocols induced positive kinematic changes as indicated by increased MTC, decreased ankle ROM, and increased hip ROM during walking in subjects with incomplete SCI.