Gait disorder rehabilitation using vision and non-vision based sensors: A systematic review

Authors

  • Asraf Ali School of Computer and Communication Engineering, Universiti Malaysia Perlis (UniMAP)
  • Kenneth Sundaraj School of Mechatronic Engineering, Universiti Malaysia Perlis (UniMAP)
  • Badlishah Ahmad School of Computer and Communication Engineering, Universiti Malaysia Perlis (UniMAP)
  • Nizam Ahamed School of Mechatronic Engineering, Universiti Malaysia Perlis (UniMAP)
  • Anamul Islam School of Computer and Communication Engineering, Universiti Malaysia Perlis (UniMAP)

DOI:

https://doi.org/10.17305/bjbms.2012.2484

Keywords:

Gait disorder, rehabilitation, sensor technology

Abstract

Even though the amount of rehabilitation guidelines has never been greater, uncertainty continues to arise regarding the efficiency and effectiveness of the rehabilitation of gait disorders. This question has been hindered by the lack of information on accurate measurements of gait disorders. Thus, this article reviews the rehabilitation systems for gait disorder using vision and non-vision sensor technologies, as well as the combination of these. All papers published in the English language between 1990 and June, 2012 that had the phrases “gait disorder” “rehabilitation”, “vision sensor”, or “non vision sensor” in the title, abstract, or keywords were identified from the SpringerLink, ELSEVIER, PubMed, and IEEE databases. Some synonyms of these phrases and the logical words “and” “or” and “not” were also used in the article searching procedure. Out of the 91 published articles found, this review identified 84 articles that described the rehabilitation of gait disorders using different types of sensor technologies. This literature set presented strong evidence for the development of rehabilitation systems using a markerless vision-based sensor technology. We therefore believe that the information contained in this review paper will assist the progress of the development of rehabilitation systems for human gait disorders.

Downloads

Download data is not yet available.
Gait disorder rehabilitation using vision and non-vision based sensors: A systematic review

Downloads

Additional Files

Published

2012-08-20

How to Cite

1.
Ali A, Sundaraj K, Ahmad B, Ahamed N, Islam A. Gait disorder rehabilitation using vision and non-vision based sensors: A systematic review. Biomol Biomed [Internet]. 2012Aug.20 [cited 2023Apr.1];12(3):193-202. Available from: https://www.bjbms.org/ojs/index.php/bjbms/article/view/2484

Issue

Vol. 12 No. 3 (2012)

Section

Reviews

INTRODUCTION

Gait is the general procedure of walking and a normal gait requires the integration of the cerebellar, sensory, visual, vestibular, muscular, basal ganglia, and auditory systems. Any abnormality in these systems can result in gait disorder, which can be rehabilitated through clinical treatment, exercise, and a few other rehabilitation systems. However, the appropriate rehabilitation system for a gait disorder requires an analysis of the gait parameters. In addition to the kinematic and kinetic gait data, it is essential to evaluate the temporospatial parameters to obtain a more accurate understanding of the gait disorder [1]. To explore the rehabilitation systems for gait disorders, we found 91 articles on human gait disorder for rehabilitation and on human motion tracking and analysis through a systematic search from the online database of highly reputable publishers. We scanned all the collected articles individually and identified and analyzed the key points of each article. We therefore discovered that the rehabilitation systems of gait disorders utilize different types of sensor technology based on the gait disorder classification. Gait disorders are classified as one of 7 types: peripheral sensory, peripheral motor, spasticity (hemiplegia, paraplegia), Parkinsonism, cerebellar palsy, cautious gait, and frontal-related gait [2]. According to the gait disorder classification, we found that, from the 91 originally identified publications, 7, 28, 21, 8, 7, 8, 3, and 2 articles discussed peripheral sensory, peripheral motor, spasticity (hemiplegia), spasticity (paraplegia), parkinsonism, cerebellar palsy, cautious, and frontal-related gait disorders, respectively. Furthermore, we determined that researchers developed the gait disorder rehabilitation systems using different types of sensor technologies, such as vision-based, non vision-based, robotics-based, and the combination of vision and non vision-based sensor technologies. In this review, we classified the sensor technologies used as vision-based sensor technology (VBST), vision plus other-based sensor technology (VOBST), and other-based sensor technology (OBST), where other refers to either non vision or robotics. We then sorted the developed rehabilitation systems according to this new sensor technology classification. We realized that some of the rehabilitation systems that use VBST utilize markers, whereas other researchers did not use any markers during the video recording. We then identified the gap in a rehabilitation system using markerless VBST for each class of gait disorders. Therefore, the future of rehabilitation research for gait disorder will focus on the use of markerless VBST.

We organized our review paper as follows. We first discuss general information on gait disorder, including the associated classifications, conditions, and syndromes. We then describe the sensor technologies that have been used to develop rehabilitation systems for gait disorders. Following this, we discuss the different keywords that were used in our systematic article searching procedure and the resulting gap finding tree. Next, we summarize the number of scanned articles and organize them by publishing date and the type of sensor technology described in the article. We also summarize the rehabilitation system used in each article according to the gait disorder classification in this section. In the subsequent section, we summarize the key points of each scanned article and discussion. Finally, we focus on future research in the field of gait disorder using markerless VBST.

Gait Disorder

From the introduction, it is clear that our main aim is to present a rehabilitation system for gait disorders. A normal procedure of walking is called a gait. Any abnormality in the cerebellar, sensory, visual, vestibular, muscular, basal ganglia, or auditory systems can result in a gait problem, or gait disorder. It has been found that the exact nature of a gait disorder depends on the particular defect in the brain, spinal cord, peripheral nerves, muscles, or bone joints [3]. Moreover, in order to determine the proper rehabilitation system for a gait disorder, it is necessary to classify the gait disorder according to its clinical view. The clinical definition of the types, conditions and syndromes of gait disorders are described in Table 1 [2].

TABLE 1: Gait disorder classification

Gait Disorder Rehabilitation Using Vision- and Non Vision-based Sensors

In the previous section, we discussed the causes and syndromes of gait disorder. The causes of a gait disorder can be recovered using a proper rehabilitation system. However, the function of a rehabilitation system is to partially or fully restore the patient's physical, sensory, and mental capabilities that resulted in the gait disorder [4]. A more extensive longitudinal study, in which the patients with gait disorders are able to cross obstacles normally after rehabilitation, is recommended to determine whether functional balance control is attained [5]. A gait disorder is one of the most common medical problems that can be recovered using the proper rehabilitation system. The rehabilitation systems for gait disorder can be developed using sensor technology. Moreover, during the rehabilitation period, the movement of the gait disorder patient needs to be assessed to determine which gait parameters are not functioning properly. Therefore, it is vital and necessary to track the gait parameters of the movement of the patient during rehabilitation. These parameters can be measured using sensor technologies to generate real-time data that dynamically represent the patient's full or partial body [6]. In this paper, we classified the measurement of human movement tracking technology on the basis of the sensor technology used, as shown in Figure 1.

FIGURE 1: Classification of human movement tracking technology based on sensor technologies.

Vision-based sensor technology (VBST)

It is difficult to evaluate the gait parameters of a patient by observing the gait cycle with the naked eye. However, VBST is a type of optical sensor that utilizes cameras to track the human movement and thus more accurately estimate the movement parameters and position. Therefore, the video recording of the gait analysis has become popular in the clinical etting for the rehabilitation of gait disorder. The VBST tracking system has been used by researchers in 3 different ways, including technologies that are marker-based, mark-erless, or a combination of marker-based and markerless.

Marker-based VBST. The marker-based VBST is a technique to track human movement through the use of optical sensors (cameras) that capture the identifier points of the human body. The marker-based tracking system reduces the hesitation of the subject movements due to the unique appearance of the marker. The most popular marker-based tracking systems in the current market are Qualisys, VICON, CODA, ReActor2, ELITE Biomech, APAS, and Polaris. However, one the major problem of using these optical sensors and markers is that it is difficult to use these to determine the exact sense joint rotation, which leads to the infeasibility of creating an accurate 3-D model of the sensed object [7].

Markerless VBST. The problem with using marker-based techniques can be solved using markerless techniques, which use external sensors, such as cameras, to track the movement parameters of the human body. The camera used should have a high resolution to ensure high accuracy [8]. Therefore, markerless vision-based sensor technology has high accuracy and compactness, computationally inexpensive and low cost. The only drawback in the use of this markerless technique is occlusion [9] and this problem can be overcome by template matching, which carries both the spatial information and the appearance of the object [10].

Combination of marker-based and markerless VBST. The combination of marker-based and markerless VBST combines the marker-based and markerless tracking systems. Because this tracking technique is not studied again in our analysis, it will not be discussed further.

Non vision-based sensor technology (NVBST)

The non-vision based sensor technology is another technique that can be used to track the human movement parameters with a sensor. In NVBST, the sensors are attached to the human body to collect the data over time. Therefore, it is possible to develop rehabilitation systems for gait disorder using NVBST. In this review, we classified the various NVBSTs used as inertial sensor, magnetic sensor, electrical sensor, or other sensor.

3.2.1 Inertial sensor. The inertial sensor technology detects and measures acceleration, angle, vibration, movement, and multiple degrees-of-freedom. The most common uses of inertial sensors are accelerometers, gyroscopes, MT9, and G-Link. A 5 sensor module, consisting of two accelerometers and one gyroscope, has been used to capture the motion of a lower limb and the results showed that the knee replacement and rehabilitation systems improved the coordination score [11]. The MT9 inertial sensor can measure the real-time “three dimensional” movements of a subject [12].

Magnetic sensor. Magnetic sensors are used to measure the speed, rotational speed, linear position, and linear angle and position in automotive, industrial and consumer applications. It provides real-time data output, rapidly capturing significant amounts of motion data. The magnetic tracking system can be used to characterize the pendulum kinematics of the leg and thus to quantify the spasticity of the quadriceps femoris muscles of stroke patients [13].

Electrical sensor. Electrical sensors examine the change in electrical or magnetic signals that occur as a result of an environmental input. Therefore, an electrical sensor can be used for the measurement of the electrical activity of muscle contraction during gait. An EMG study was used for the clinical analysis of qualitative gait evaluation based on the repetition, symmetry, and smoothness characteristics of the activation patter of the walking muscle [14]. The EMG measurements showed that muscle weakness and lack of reflex adaptation can result in wrist joint stiffness during an active posture task [15].

Others sensors. The gait speed can be analyzed by using a sensor to assess the walking performance of gait disorder patients. The mean gait speed and the temporal symmetry ratio during each two-minute interval of a 6-minute walk test were examined using a pressure-sensitive mat [16]. The ground reaction force measurement platform (Kistler 9281B) sensor was used to apply artificial neural networks for the identification of gait malfunction [17]. The M3D sensor system, which integrates a mobile force plate, 3D motion analysis units and a wireless data logger, was used to obtain 3D motion and force data on the gait of a patient in various walking environments. A quantitative gait analysis method based on these ambulatory measurements is proposed for the implementation of human lower limb kinematic and kinetic analyses [18]. The pressure sensitive GAITRite system was used to determine the effect of muscle fatigue on gait characteristics under single and dual-task conditions in young and older adults. This study found that muscle fatigue significantly decreased the single-task gait velocity and stride length in young adults and significantly increased the dual-task gait velocity and stride length in older adults [19]. The temporal and spatial gait parameters, including self-selected velocity, cadence, stance time, swing time, double support time, step length, and width of the support base, were assessed through the use of an electronic gait mat (gait Mat II, EQ Inc.) [20].

METHODOLOGY

We used a systematic searching procedure to identify articles on gait rehabilitation from an online digital database of highly reputable publishers. We used a few keywords and their synonyms in combination with some logical operators in our searching procedure. These search terms are listed in List 1. We then selected those papers that were published in English from the year 1990 to June-2012. After collecting the articles, we scanned each article individually and identified its key points, which we then used to draw the gap analysis tree shown in Figure 2. We then analyzed all the collected articles according to Figure 2. From the bottom of the gap analysis tree, we determined which systems have been developed and found out that there is still a large amount of research required on the development of a system for the rehabilitation of gait disorder using markerless VBST.

List 1: Overview of the search terms used in the article collection procedure

  • Gait Analysis-Review

  • Survey of Gait Analysis

  • Clinical Gait Analysis-Review

  • Survey of Clinical Gait Analysis

  • Gait Analysis AND Rehabilitation-Review

  • Survey of Gait Analysis AND Rehabilitation

  • Gait Disorder Analysis AND Rehabilitation-Review

  • Survey of Gait Disorder Analysis AND Rehabilitation

  • Gait Analysis

  • Gait Analysis AND Rehabilitation

  • Gait Disorder

  • Gait Disorder Rehabilitation

  • Gait Disorder Analysis

  • Gait Disorder Analysis AND Rehabilitation

  • Human Motion Tracking

  • Human Motion Tracking AND Gait Analysis

  • Human Motion Tracking OR Gait Analysis

  • Human Movement Tracking

  • Human Movement Tracking AND Gait Analysis

  • Human Movement Tracking OR Gait Analysis

  • Gait Disorder AND Clinical

  • Gait Disorder Rehabilitation AND Clinical

  • Clinical Gait Analysis

  • Clinical Gait Analysis AND Rehabilitation

FIGURE 2: Gap analysis tree.

RESULTS

Using our systematic article searching procedure, we found 91 articles that have published in highly reputable journals between 1990 and June, 2012. These articles were then organized according to the publishing date and the sensor technology used; the numbers of respective articles for each category are shown in a tabulated format in Table 2. From the 91 articles collected, we found 84 articles that discuss gait disorders and 7 that were not related to gait disorders. Therefore, we did not consider the latter in further analysis. The outcomes of the 84 articles on gait disorder are summarized according to the gap analysis tree that was drawn (Figure 2).

TABLE 2: Articles organized by publishing date and sensor technology

“Peripheral sensory”gait disorder

There were 7 articles on peripheral sensory gait disorder. Of these, 3, 3, and 1 articles described rehabilitation systems for this disorder using VBST, VOBST, and OBST, respectively. The article that described the use of OBST involved a gait disorder with unsteady balance perturbation [9], whereas the articles that discussed the use of VOBST focused on gait disorders with symptoms: unsteady of fall control [1, 21], and unsteady of balance control [22]. In addition, out of the 3 articles that discussed VBST, 2 articles used marker-based technology and 1 utilized markerless technology. The articles that discussed the development of marker-based VBST discussed gait disorders with symptoms of unsteady balance control [5] and the inability to walk in a straight line [23], whereas the article on markerless VBST focused on an unsteady gait cycle [24].

“Peripheral motor” gait disorder

We found 28 articles on peripheral motor gait disorder. Of these, 10, 8 and 10 manuscripts reported the use of VBST, VOBST, and OBST, respectively. Those articles that discussed the use of OBST described the use of this technique in the rehabilitation of knee and hip neuropathy [11], muscle weakness dystrophy [14, 15, 19], ankle dorsiflexor slapping [25], foot slapping [26], spinal cord steppage [27], foot drop steppage [28], weight bearing motor control [29], and knee arthritis [30]. The articles that discuss the use of VOBST focus on the following gait disorder symptoms: steppage of central cord syndrome [31], motor control of foot control [32], slapping of toe clearance and velocity [33], motor control of Prader-Willi, Down syndrome [34], dystrophic of muscle weakness [35, 36], neuropathy of motor fatigue [37], and neuropathy of chronic low back pain [38]. Of the 10 articles on VBST, 8 articles discuss marker–based VBST and 2 articles focus on markerless VBST. The articles on marker-based VBST discuss motor control of abnormal gait [39], neuropathy of ankle dossal-planter-flexion [40], motor control of weight bearing [41], neuropathy of lower limb joint [42, 43], motor control of cerebral palsy [44], arthritis of trunk control in children [45], and neuropathy of pelvis [46], whereas the articles on markerless VBST focus on the motor function after stroke [47] and the motor neuropathy of autism [48].

“Spasticity (Hemiplegia)”gait disorder

A total of 21 articles discuss the rehabilitation of spasticity (Hemiplegia) gait disorder. Of these 21 articles, 7, 9, and 5 describe the use of VBST, VOBST, and OBST, respectively, on the rehabilitation of this type of gait disorder. The latter 5 articles describe the use of OBST on the rehabilitation of the hemiplegia of the spinal cord [12], lower limb [49], ankle-foot [20], knee in the stance phase [50], and the ankle [51], whereas the 9 articles that describe the use of VOBST focus on the hemiplegia of the leg swing [52], multi-joint leg extension [53], lower limb [54, 55], knee rotation [56, 57], hip rotation [58], knee [59], and the knee and pelvis [60]. All 7 articles that discuss VBST describe the use of marker-based VBST for the rehabilitation of the hemiplegia of the leg movement [61, 62], ankle and subtalar joint [63], lower limb [64, 65], feet [66], and intralimb [67].”

“Spasticity (Paraplegia)” gait disorder

We collected 8 articles on spasticity (paraplegia) gait disorder. Of these, 3, 2, and 3 articles describe the use of VBST, VOBST, and OBST, respectively, on the rehabilitation systems used. The articles that describe the use of OBST on the rehabilitation of this type of gait disorder focus on the paraplegia of the lower limbs [13, 18] and cerebral palsy [68]. The 2 VOBST articles discuss the rehabilitation of the paraplegia of the stiff knee [69], and cerebral palsy [70]. In addition, there are 1 and 2 articles that describe the use of marker-based and markerless VBST, respectively, for the rehabilitation of paraplegia. The marker-based VBST was developed for the rehabilitation of paraplegia of the stiff knee [71], whereas the markerless VBST was used for the rehabilitation of paraplegia of the legs [72] and the lower limbs [73].

“Parkinsonism” gait disorder

A total of 7 articles were found on parkinsonism gait disorder. Of these, 2, 2, and 3 articles discuss the use of VBST, VOBST, and OBST, respectively. The OBST articles focus on the parkinsonism of the gait speed [16], body movement [74], and motor fluctuations [75], whereas the VOBST articles describe the rehabilitation of parkinsonism of the shoulder joint [76] and stiff lower limbs [77]. The 2 VBST articles describe the use of marker-based VBST for the rehabilitation of parkinsonism of the stride (length, duration, velocity) [78] and of the posture and gait cycle [79].

“Cerebellar” gait disorder

We found 8 articles on cerebellar gait disorder. Of these 8 articles, 4, 3, and 1 discuss the use of VBST, VOBST, and OBST, respectively. The OBST article focused on a gait disorder with gestural ataxia [80]. The VOBST articles describe the rehabilitation of ataxic of cerebral palsy [3] and ataxic of trunk movement [81, 82]. Out of the 4 VBST articles, 3 discuss the development of marker-based VBST, whereas 1 article described the use of markerless VBST. The marker-based VBST was used for the rehabilitation of ataxic of trunk movement [83], ataxic of upper body [84], and ataxic of stopping posture [85]. The article on markerless VBST discusses ataxic of the upper limb [86].

“Cautious” gait disorder

A total of 3 articles discuss the cautious gait disorder. Of these, one article reports the use of a VOBS system and the other two mention OBST technology. One important issue related to the use of OBST and VOBST in the rehabilitation of cautious gait disorder is the chance of the patient falling down [87-89].

“Frontal-related” gait disorder

We found 2 articles on frontal-related gait disorder, 1 of which describes the use of VBST and the other the use of VOBST for the rehabilitation of this type of gait disorder. The VOBST article focuses on the rehabilitation of the speed and short step of the frontal gait [90], whereas the VBST article discusses the use of a marker-based VBST for the rehabilitation of the foot clearance of the frontal gait [91].

DISCUSSION

We collected 91 published articles for the analysis of the current procedures used in the rehabilitation of gait disorder to identify future research in this field. We scanned all the articles individually and identified the key points of each. The key points of each articles only which related to gait disorder are summarized in Table 3. Finally the summarized of key findings of this paper are presented as follows:

  1. Application area of rehabilitation systems for gait disorder are motor function, brain, leg, foot, ankle, knee, postural, finger, spinal cord, balance perturbation, hip, wrist, physical activity for overweight, upper limbs, and muscles.

  2. Gait disorder types are peripheral sensory, peripheral motor, spasticity (hemiplegia, paraplegia), Parkinsonism, cerebellar palsy, cautious gait, and frontal-related gait.

  3. Gait disorder causes are cerebellar, sensory, visual, vestibular, muscular, basal ganglia, and auditory systems.

  4. A total of 84 articles were related to gait disorder out of 91 collected articles.

  5. In 84 related to gait disorder articles, 25 were utilized OBST such as electrical, magnetic, inertial, robotic and different types of pressure sensitive sensors; 29 were utilized VOBST such as Qualisys, VICON, CODA, ReActor2, ELITE Biomech, APAS, Polaris motion capture system, and video camera with the differents types of presure sensitive sensor or OBST; and 30 were utilized only VBST such as video camera, and Qualisys, VICON, CODA, ReActor2, ELITE Biomech, APAS, and Polaris motion capture system.

  6. Out of 30 VBST articles, 24 were utilized marker to track the interest point of the subject during motion capture whereas 6 were not used any marker.

TABLE 3: The key points of each article

We observed that most of the existing rehabilitation systems for the specific class of gait disorder were utilized vision and non-vision sensor technologies to track and analysis the motion of the patients. Most of these motion tracking systems need experts to perform calibration and sampling for developing the rehabilitation systems. Without good calibration and sampling, and also without the help of the experts, rehabilitation systems cannot work properly. These types of rehabilitation systems cannot be user friendly for the patients to recover their disorder. Another vital point is cost. People's intention is getting accurate result and reduces their cost. But researchers planned to build a complex motion tracking system with the aim of satisfy multiple purposes. This enforces costly mechanism to develop a rehabilitation system. These types of rehabilitation systems are not suitable for the people due to more expensive. We also identify that some of the existing rehabilitation systems of gait disorder required large spaces during the recovery period. As an importance, this is one more obstacle for the people who don’t have more accommodation space for the rehabilitation of gait disorder. Another obstacle is real time, example, some patients with hearing problem may require visual advice, and other with visual problem may need auditory signal. In this point of view, it is also needed a simple system that require to specify accurate or wrong movement of the patients during motion capture. This system allocates the patients to adjust his movements right away for getting exact result. The application of a device is very important. Most of the patients, who had suffered trouble with gait, have significant loss of function of their affected part. In this case, it is recommended that device should be as easy as possible to be appropriate for the patients. This problem can be overcome by a good interface between patients and computer in both motion tracking and its application in rehabilitation system. From a practical point of view, an attractive interface can encourage patients to carry out device manipulation. In summary, for the rehabilitation system of gait disorder, it is needed to consider of cost, size, operation, device manipulation, using space, and automated monitoring system.

CONCLUSION

A number of systems have been developed for the rehabilitation of gait disorders. The evidence shows it is crucial to measure real-time movement data to determine the correct rehabilitation system for an individual gait disorder. The real-time data can be established using a proper monitoring system. A rehabilitation system was not developed for the monitoring of parkinsonism gait disorder patients using a markerless vision-based sensor technology. Therefore, we propose the development of a proper monitoring system for the rehabilitation of parkinsonism gait disorder using a markerless vision-based sensor technology. This proposed user-friendly, economical, portable and automatic monitoring system will potentially partially or fully rehabilitate patients with parkinsonism gait disorder.

DECLARATION OF INTEREST

The authors declare no conflict of interest.

REFERENCES

  1. , , (). Gait disorders and falls. GeroPsych: The Journal of Gerontopsychology and Geriatric Psychiatry.
  2. , , (). Ambulatory devices for chronic gait disorders in the elderly. Am Fam Physician.
  3. (). Clinical gait analysis: A review. Human Movement Science.
  4. , , , , , (). Recent Survey of Automated Rehabilitation Systems Using EMG Biosensors. Journal of Physical Therapy Science.
  5. , , (). Different gait tasks distinguish immediate vs. long-term effects of concussion on balance control. Journal of NeuroEngineering and Rehabilitation.
  6. , , , , , (). Characteristics in human motion - from acquisition to analysis. IEEE International Conference on Humanoid Robots.
  7. , (). A Survey of Glove-based Input. IEEE Comput Graph Appl.
  8. (). Position trackers for Head Mounted Display systems: A survey.
  9. , (). Balance perturbation system to improve balance compensatory responses during walking in old persons. Journal of NeuroEngineering and Rehabilitation.
  10. , , , (). Robust Edge-Enhanced Fragment Based Normalized Correlation Tracking in Cluttered and Occluded Imagery. Signal Processing, Image Processing and Pattern Recognition.
  11. , , (). A New Approach for Quantitative Analysis of Inter-Joint Coordination During Gait.
  12. , , , (). Development of a real-time three-dimensional spinal motion measurement system for clinical practice. Medical and Biological Engineering and Computing. 2006/12/01
  13. , , (). Reliability and validity of pendulum test measures of spasticity obtained with the Polhemus tracking system from patients with chronic stroke. Journal of NeuroEngineering and Rehabilitation.
  14. , (). Qualitative evaluations of gait rehabilitation via EMG muscle activation pattern: Repetition, symmetry, and smoothness. Robotics and Biomimetics (ROBIO).
  15. , , , , , (). Muscle weakness and lack of reflex gain adaptation predominate during post-stroke posture control of the wrist. Journal of NeuroEngineering and Rehabilitation.
  16. , , , , (). Changes in spatiotemporal gait variables over time during a test of functional capacity after stroke. Journal of NeuroEngineering and Rehabilitation.
  17. , , (). Clinical gait analysis by neural networks: issues and experiences.
  18. , , , (). Three-dimensional lower limb kinematic and kinetic analysis based on a wireless sensor system.
  19. , , , , (). Effects of muscle fatigue on gait characteristics under single and dual-task conditions in young and older adults. Journal of NeuroEngineering and Rehabilitation.
  20. , , , (). The Effect of an Ankle-Foot Orthosis on Temporal Spatial Parameters and Asymmetry of Gait in Hemiparetic Patients. PM & R: the journal of injury, function, and rehabilitation.
  21. , , , , , (). Clinical implications of gait analysis in the rehabilitation of adult patients with “Prader-Willi” Syndrome: a cross-sectional comparative study (“Prader-Willi” Syndrome vs matched obese patients and healthy subjects). Journal of NeuroEngineering and Rehabilitation.
  22. , , , , (). Constraints on the kinetic, kinematic and spatiotemporal parameters of gait in Parkinson's disease. Human Movement Science.
  23. , , , (). A force-based approach for joint efforts estimation during the double support phase of gait. Procedia IUTAM.
  24. , (). A Monocular Marker-Free Gait Measurement System. Neural Systems and Rehabilitation Engineering.
  25. (). Simulations of foot stability during gait characteristic of ankle dorsiflexor weakness in the elderly. Neural Systems and Rehabilitation Engineering.
  26. , (). Clinical applications of plantar pressure measurement in pediatric orthopedics. Pediatric Gait 2000.
  27. , , (). The effects of powered ankle-foot orthoses on joint kinematics and muscle activation during walking in individuals with incomplete spinal cord injury. BioMed Central.
  28. , , (). Support vector machine for classification of walking conditions of persons after stroke with dropped foot. Human Movement Science.
  29. , , , , , (). Physical activity monitoring in obese people in the real life environment. Journal of NeuroEngineering and Rehabilitation.
  30. , (). Gait Analysis in Patients with Knee Osteoarthritis Walking at Normal Speed on the Flat Ground. IFMBE Proceeding.
  31. , , , , , (). Gait kinematic analysis in patients with a mild form of central cord syndrome. Journal of NeuroEngineering and Rehabilitation.
  32. , , , , , (). Characterizing multisegment foot kinematics during gait in diabetic foot patients. Journal of NeuroEngineering and Rehabilitation.
  33. , , , , (). Toe clearance and velocity profiles of young and elderly during walking on sloped surfaces. Journal of NeuroEngineering and Rehabilitation.
  34. , , , , , (). Gait patterns in Prader-Willi and Down syndrome patients. Journal of NeuroEngineering and Rehabilitation.
  35. , , (). Measurement and simulation of joint motion induced via biarticular muscles during human walking. Procedia IUTAM.
  36. , , , , , (). Abnormal muscle activation during gait in diabetes patients with and without neuropathy. Gait & posture.
  37. , , , , , (). Objective assessment of motor fatigue in multiple sclerosis using kinematic gait analysis: a pilot study. Journal of Neuro Engineering and Rehabilitation.
  38. , , (). Biomechanical comparison of isokinetic lifting and free lifting when applied to chronic low back pain rehabilitation. Medical and Biological Engineering and Computing.
  39. , , , (). Abnormal Gait Detection Using Discrete Fourier Transform.
  40. , , , , , (). Simultaneous measurements of kinematics and fMRI: compatibility assessment and case report on recovery evaluation of one stroke patient. Journal of NeuroEngineering and Rehabilitation.
  41. , , , , , (). Effects of obesity and chronic low back pain on gait. Journal of NeuroEngineering and Rehabilitation.
  42. (). A new intelligent model for automated assessment of elder gait change. Biomedical Engineering and Informatics (BMEI).
  43. , , , , (). Gait status 17-26 years after selective dorsal rhizotomy. Gait & posture.
  44. , , , (). An automated method for analysis of gait data to aid clinical interpretation. Biomedical Engineering (MECBME).
  45. , , , , (). Trunk and center of mass movements during gait in children with juvenile idiopathic arthritis. Human Movement Science.
  46. , (). Three-dimensional relationships between the movements of the pelvis and lumbar spine during normal gait. Human Movement Science.
  47. , (). Human motion tracking for rehabilitation—A survey. Biomedical Signal Processing and Control.
  48. , , , , (). Movement analysis in infancy may be useful for early diagnosis of autism.
  49. , , , , (). Preliminary study for gait phases detection to develop a rehabilitation equipment for hemiplegic patients.
  50. , , , , , (). Anteroposterior stability of the knee during the stance phase of gait after anterior cruciate ligament deficiency. Gait & posture.
  51. , , (). Non-invasive brain stimulation enhances fine motor control of the hemiparetic ankle: implications for rehabilitation. Exp Brain Res. 2011/03/01
  52. , , , , (). Corcos Control of interjoint coordination during the swing phase of normal gait at different speeds. Journal of NeuroEngineering and Rehabilitation.
  53. , , , , (). Knee and ankle joint torque–angle relationships of multi-joint leg extension. Journal of Biomechanics.
  54. , , , , (). Assessment of gait improvement surgery in diplegic children using computerised gait analysis. European Surgery.
  55. , (). Walking patterns change rapidly following asymmetrical lower extremity loading. Human Movement Science.
  56. , , , , , (). On the estimation of knee joint kinematics. Human Movement Science.
  57. , , (). Measurement of an intact knee kinematics using gait and fluoroscopic analysis. Knee Surgery, Sports Traumatology, Arthroscopy.
  58. , , (). A new approach to determine the hip rotation profile from clinical gait analysis data. Human Movement Science.
  59. , , , , (). Gait assessment in unicompartmental knee arthroplasty patients: Principal component modelling of gait waveforms and clinical status. Human Movement Science.
  60. , (). Visualisation of Gait Analysis Data.
  61. , (). On skin movement artefact-resonant frequencies of skin markers attached to the leg. Human Movement Science.
  62. , , , (). A low-cost video-based tool for clinical gait analysis. Engineering in Medicine and Biology Society.
  63. , , , (). Development of clinical measurement of the axes of rotation of the ankle and subtalar joints. Engineering in Medicine and Biology.
  64. , , (). The evolution of methods for the capture of human movement leading to markerless motion capture for biomechanical applications. Journal of NeuroEngineering and Rehabilitation.
  65. , , , , , (). The influence of a long term exercise program on lower limb movement variability and walking performance in patients with peripheral arterial disease. Human Movement Science.
  66. , (). The effects of low arched feet on lower limb gait kinematics in children. The Foot.
  67. , , , (). An examination of constraints affecting the intralimb coordination of hemiparetic gait. Human Movement Science.
  68. , , , , , (). uantitative study of walker-assisted gait in children with cerebral palsy: anterior versus posterior walkers Pediatric Gait: A new Millennium in Clinical Care and Motion Analysis Technology.
  69. , (). Kinetics of stiff-legged gait: induced acceleration analysis. Rehabilitation Engineering, IEEE Transactions on.
  70. , (). Gait asymmetries in children with cerebral palsy: Do they deteriorate with running?. Gait & posture.
  71. , , , , , (). Feasibility Experiment of Gait Training System Using Real-time Visual Feedback of Knee Joint Angle 14th Nordic-Baltic Conference on Biomedical Engineering and Medical Physics.
  72. , , (). A 3-D marker-free system for the analysis of movement disabilities - an application to the legs. Information Technology in Biomedicine.
  73. , , (). Depth data based capture of human movement for biomechanical application in clinical rehabilitation use. Health Informatics and Bioinformatics (HIBIT).
  74. , , , (). Walking to the beat of different drums: Practical implications for the use of acoustic rhythms in gait rehabilitation. Gait & posture.
  75. , , , , , (). Monitoring Motor Fluctuations in Patients With Parkinson & 's Disease Using Wearable Sensors. Information Technology in Biomedicine, IEEE Transactions on.
  76. (). Indirect biomechanics measurement on shoulder joint moments of walker-assisted gait. Virtual Environments, Human-Computer Interfaces and Measurements Systems, 2009.
  77. , , , , , (). Changes in lower limb co-contraction and stiffness by toddlers with Down syndrome and toddlers with typical development during the acquisition of independent gait. Human Movement Science.
  78. , , , , , (). Effects of 6-month, Multimodal Exercise Program on Clinical and Gait Parameters of Patients with Idiopathic Parkinson's Disease: A Pilot Study. ISRN Neurology.
  79. , , (). Video Analysis of Human Gait and Posture to Determine Neurological Disorders. EURASIP Journal on Image and Video Processing.
  80. , (). Enabling gestural interaction by means of tracking dynamical systems models and assistive feedback. Systems, Man and Cybernetics.
  81. , , , (). Critical kinematic events occurring in the trunk during walking. Human Movement Science.
  82. , (). Kinematic variability and local dynamic stability of upper body motions when walking at different speeds. Journal of Biomechanics.
  83. , , , , (). Kinematic aspects of trunk motion and gender effect in normal adults. Journal of NeuroEngineering and Rehabilitation.
  84. , , , (). Control of the upper body accelerations in young and elderly women during level walking. Journal of NeuroEngineering and Rehabilitation.
  85. , , , , (). Development of postural control during gait in typically developing children: The effects of dual-task conditions. Gait & posture.
  86. , , , (). A neural tracking and motor control approach to improve rehabilitation of upper limb movements. Journal of NeuroEngineering and Rehabilitation.
  87. , , , , , (). Human motion tracking and gait analysis: a brief review of current sensing systems and integration with intelligent environments. CORD Conference Proceedings.
  88. , , (). Exercise training can improve spatial characteristics of time-critical obstacle avoidance in elderly people. Human Movement Science.
  89. , , , , (). Lighting and perceptual cues: Effects on gait measures of older adults at high and low risk for falls. BMC Geriatrics.
  90. , , , (). Aging Alters Joint Power Generation across a Range of Gait Speeds in Healthy Elderly 6th World Congress of Biomechanics (WCB 2010) August 1-6, 2010 Singapore.
  91. , , (). Prediction of foot clearance parameters as a precursor to forecasting the risk of tripping and falling. Human Movement Science.