FP-GCN: Frequency Pyramid Graph Convolutional Network for Enhancing Pathological Gait Classification.

Gait, a manifestation of one's walking pattern, intricately reflects the harmonious interplay of various bodily systems, offering valuable insights into an individual's health status. However, the current study has shortcomings in the extraction of temporal and spatial dependencies in joint motion, resulting in inefficiencies in pathological gait classification. In this paper, we propose a Frequency Pyramid Graph Convolutional Network (FP-GCN), advocating to complement temporal analysis and further enhance spatial feature extraction. specifically, a spectral decomposition component is adopted to extract gait data with different time frames, which can enhance the detection of rhythmic patterns and velocity variations in human gait and allow a detailed analysis of the temporal features. Furthermore, a novel pyramidal feature extraction approach is developed to analyze the inter-sensor dependencies, which can integrate features from different pathways, enhancing both temporal and spatial feature extraction. Our experimentation on diverse datasets demonstrates the effectiveness of our approach. Notably, FP-GCN achieves an impressive accuracy of 98.78% on public datasets and 96.54% on proprietary data, surpassing existing methodologies and underscoring its potential for advancing pathological gait classification. In summary, our innovative FP-GCN contributes to advancing feature extraction and pathological gait recognition, which may offer potential advancements in healthcare provisions, especially in regions with limited access to medical resources and in home-care environments. This work lays the foundation for further exploration and underscores the importance of remote health monitoring, diagnosis, and personalized interventions.

Investigation of neural and biomechanical impairments leading to pathological toe and heel gaits using neuromusculoskeletal modelling.

This study investigates the pathological toe and heel gaits seen in human locomotion using neuromusculoskeletal modelling and simulation. In particular, it aims to investigate potential cause-effect relationships between biomechanical or neural impairments and pathological gaits. Toe and heel gaits are commonly present in spinal cord injury, stroke and cerebral palsy. Toe walking is mainly attributed to spasticity and contracture at plantar flexor muscles, whereas heel walking can be attributed to muscle weakness of biomechanical or neural origin. To investigate the effect of these impairments on gait, this study focuses on the soleus and gastrocnemius muscles as they contribute to ankle plantarflexion. We built a reflex circuit model based on previous work by Geyer and Herr with additional pathways affecting the plantar flexor muscles. The SCONE software, which provides optimisation tools for 2D neuromechanical simulation of human locomotion, is used to optimise the corresponding reflex parameters and simulate healthy gait. We then modelled various bilateral plantar flexor biomechanical and neural impairments, and individually introduced them in the healthy model. We characterised the resulting simulated gaits as pathological or not by comparing ankle kinematics and ankle moment with the healthy optimised gait based on metrics used in clinical studies. Our simulations suggest that toe walking can be generated by hyperreflexia, whereas muscle and neural weaknesses partially induce heel gait. Thus, this 'what if' approach is deemed of great interest as it allows investigation of the effect of various impairments on gait and suggests an important contribution of active reflex mechanisms to pathological toe gait. KEY POINTS: Pathological toe and heel gaits are commonly present in various conditions such as spinal cord injury, stroke and cerebral palsy. These conditions present various neural and biomechanical impairments, but the cause-effect relationships between these impairments and pathological gaits are difficult to establish clinically. Based on neuromechanical simulation, this study focuses on the plantar flexor muscles and builds a new reflex circuit controller to model and evaluate the potential effect of both neural and biomechanical impairments on gait. Our results suggest an important contribution of active reflex mechanisms to pathological toe gait. This 'what if' based on neuromechanical modelling is thus deemed of great interest to target potential causes of pathological gait.

Dynamic stability during gait in idiopathic normal pressure hydrocephalus and Parkinson's disease.

OBJECTIVES: To clarify a characteristic of dynamic stability during gait in idiopathic normal pressure hydrocephalus (iNPH) and Parkinson's disease (PD), and to explore the association between dynamic stability and disease severity in each disease. MATERIALS AND METHODS: The 5-m gait of 36 iNPH (precerebrospinal fluid drainage), 20 PD (medicated state), and 25 healthy controls (HC) were evaluated using three-dimensional motion analysis. Ambulatory dynamic stability was defined as the ability to maintain the extrapolated center of mass within the base of support at heel contact, with the distance between the two referred to as the margin of stability (MOS). RESULTS: Anteroposterior direction (AP) MOS was significantly larger in the iNPH and PD groups than in the HC group; no significant difference was found between the iNPH and PD groups. Mediolateral direction (ML) MOS was significantly larger in the iNPH and PD groups than in the HC group and significantly larger in the iNPH group than in the PD group. In the iNPH group, the disease severity was positively correlated with only ML MOS. In the PD group, the disease severity was positively correlated with the AP MOS and ML MOS. CONCLUSIONS: Dynamic stability in iNPH increases in AP and ML, and it may be associated with not only iNPH-associated gait disturbance but also with a voluntarily cautious gait strategy. Dynamic stability in PD only increased in AP, and this may be associated with PD symptoms. These findings will help physicians understand the difference in pathological gait including dynamic stability between patients with iNPH and PD.

Performance of Deep Learning Models in Forecasting Gait Trajectories of Children with Neurological Disorders.

Forecasted gait trajectories of children could be used as feedforward input to control lower limb robotic devices, such as exoskeletons and actuated orthotic devices (e.g., Powered Ankle Foot Orthosis-PAFO). Several studies have forecasted healthy gait trajectories, but, to the best of our knowledge, none have forecasted gait trajectories of children with pathological gait yet. These exhibit higher inter- and intra-subject variability compared to typically developing gait of healthy subjects. Pathological trajectories represent the typical gait patterns that rehabilitative exoskeletons and actuated orthoses would target. In this study, we implemented two deep learning models, a Long-Term Short Memory (LSTM) and a Convolutional Neural Network (CNN), to forecast hip, knee, and ankle trajectories in terms of corresponding Euler angles in the pitch, roll, and yaw form for children with neurological disorders, up to 200 ms in the future. The deep learning models implemented in our study are trained on data (available online) from children with neurological disorders collected by Gillette Children's Speciality Healthcare over the years 1994-2017. The children's ages range from 4 to 19 years old and the majority of them had cerebral palsy (73%), while the rest were a combination of neurological, developmental, orthopaedic, and genetic disorders (27%). Data were recorded with a motion capture system (VICON) with a sampling frequency of 120 Hz while walking for 15 m. We investigated a total of 35 combinations of input and output time-frames, with window sizes for input vectors ranging from 50-1000 ms, and output vectors from 8.33-200 ms. Results show that LSTMs outperform CNNs, and the gap in performance becomes greater the larger the input and output window sizes are. The maximum difference between the Mean Absolute Errors (MAEs) of the CNN and LSTM networks was 0.91 degrees. Results also show that the input size has no significant influence on mean prediction errors when the output window is 50 ms or smaller. For output window sizes greater than 50 ms, the larger the input window, the lower the error. Overall, we obtained MAEs ranging from 0.095-2.531 degrees for the LSTM network, and from 0.129-2.840 degrees for the CNN. This study establishes the feasibility of forecasting pathological gait trajectories of children which could be integrated with exoskeleton control systems and experimentally explores the characteristics of such intelligent systems under varying input and output window time-frames.

Mechanical work as a (key) determinant of energy cost in human locomotion: recent findings and future directions.

NEW FINDINGS: What is the topic of this review? This narrative review explores past and recent findings on the mechanical determinants of energy cost during human locomotion, obtained by using a mechanical approach based on König's theorem (Fenn's approach). What advances does it highlight? Developments in analytical methods and their applications allow a better understanding of the mechanical-bioenergetic interaction. Recent advances include the determination of 'frictional' internal work; the association between tendon work and apparent efficiency; a better understanding of the role of energy recovery and internal work in pathological gait (amputees, stroke and obesity); and a comprehensive analysis of human locomotion in (simulated) low gravity conditions. ABSTRACT: During locomotion, muscles use metabolic energy to produce mechanical work (in a more or less efficient way), and energetics and mechanics can be considered as two sides of the same coin, the latter being investigated to understand the former. A mechanical approach based on König's theorem (Fenn's approach) has proved to be a useful tool to elucidate the determinants of the energy cost of locomotion (e.g., the pendulum-like model of walking and the bouncing model of running) and has resulted in many advances in this field. During the past 60 years, this approach has been refined and applied to explore the determinants of energy cost and efficiency in a variety of conditions (e.g., low gravity, unsteady speed). This narrative review aims to summarize current knowledge of the role that mechanical work has played in our understanding of energy cost to date, and to underline how recent developments in analytical methods and their applications in specific locomotion modalities (on a gradient, at low gravity and in unsteady conditions) and in pathological gaits (asymmetric gait pathologies, obese subjects and in the elderly) could continue to push this understanding further. The recent in vivo quantification of new aspects that should be included in the assessment of mechanical work (e.g., frictional internal work and elastic contribution) deserves future research that would improve our knowledge of the mechanical-bioenergetic interaction during human locomotion, as well as in sport science and space exploration.

Validation of quantitative gait analysis systems for Parkinson's disease for use in supervised and unsupervised environments.

BACKGROUND: Gait impairments are among the most common and impactful symptoms of Parkinson's disease (PD). Recent technological advances aim to quantify these impairments using low-cost wearable systems for use in either supervised clinical consultations or long-term unsupervised monitoring of gait in ecological environments. However, very few of these wearable systems have been validated comparatively to a criterion of established validity. OBJECTIVE: We developed two movement analysis solutions (3D full-body kinematics based on inertial sensors, and a smartphone application) in which validity was assessed versus the optoelectronic criterion in a population of PD patients. METHODS: Nineteen subjects with PD (7 female) participated in the study (age: 62 ± 12.27 years; disease duration: 6.39 ± 3.70 years; HY: 2 ± 0.23). Each participant underwent a gait analysis whilst barefoot, at a self-selected speed, for a distance of 3 times 10 m in a straight line, assessed simultaneously with all three systems. RESULTS: Our results show excellent agreement between either solution and the optoelectronic criterion. Both systems differentiate between PD patients and healthy controls, and between PD patients in ON or OFF medication states (normal difference distributions pooled from published research in PD patients in ON and OFF states that included an age-matched healthy control group). Fair to high waveform similarity and mean absolute errors below the mean relative orientation accuracy of the equipment were found when comparing the angular kinematics between the full-body inertial sensor-based system and the optoelectronic criterion. CONCLUSIONS: We conclude that the presented solutions produce accurate results and can capture clinically relevant parameters using commodity wearable sensors or a simple smartphone. This validation will hopefully enable the adoption of these systems for supervised and unsupervised gait analysis in clinical practice and clinical trials.

Machine-learning-based children's pathological gait classification with low-cost gait-recognition system.

BACKGROUND: Pathological gaits of children may lead to terrible diseases, such as osteoarthritis or scoliosis. By monitoring the gait pattern of a child, proper therapeutic measures can be recommended to avoid the terrible consequence. However, low-cost systems for pathological gait recognition of children automatically have not been on market yet. Our goal was to design a low-cost gait-recognition system for children with only pressure information. METHODS: In this study, we design a pathological gait-recognition system (PGRS) with an 8 × 8 pressure-sensor array. An intelligent gait-recognition method (IGRM) based on machine learning and pure plantar pressure information is also proposed in static and dynamic sections to realize high accuracy and good real-time performance. To verifying the recognition effect, a total of 17 children were recruited in the experiments wearing PGRS to recognize three pathological gaits (toe-in, toe-out, and flat) and normal gait. Children are asked to walk naturally on level ground in the dynamic section or stand naturally and comfortably in the static section. The evaluation of the performance of recognition results included stratified tenfold cross-validation with recall, precision, and a time cost as metrics. RESULTS: The experimental results show that all of the IGRMs have been identified with a practically applicable degree of average accuracy either in the dynamic or static section. Experimental results indicate that the IGRM has 92.41% and 97.79% intra-subject recognition accuracy, and 85.78% and 78.81% inter-subject recognition accuracy, respectively, in the static and dynamic sections. And we find methods in the static section have less recognition accuracy due to the unnatural gesture of children when standing. CONCLUSIONS: In this study, a low-cost PGRS has been verified and realize feasibility, highly average precision, and good real-time performance of gait recognition. The experimental results reveal the potential for the computer supervision of non-pathological and pathological gaits in the plantar-pressure patterns of children and for providing feedback in the application of gait-abnormality rectification.

Wearable Sensor-Based Real-Time Gait Detection: A Systematic Review.

Gait analysis has traditionally been carried out in a laboratory environment using expensive equipment, but, recently, reliable, affordable, and wearable sensors have enabled integration into clinical applications as well as use during activities of daily living. Real-time gait analysis is key to the development of gait rehabilitation techniques and assistive devices such as neuroprostheses. This article presents a systematic review of wearable sensors and techniques used in real-time gait analysis, and their application to pathological gait. From four major scientific databases, we identified 1262 articles of which 113 were analyzed in full-text. We found that heel strike and toe off are the most sought-after gait events. Inertial measurement units (IMU) are the most widely used wearable sensors and the shank and foot are the preferred placements. Insole pressure sensors are the most common sensors for ground-truth validation for IMU-based gait detection. Rule-based techniques relying on threshold or peak detection are the most widely used gait detection method. The heterogeneity of evaluation criteria prevented quantitative performance comparison of all methods. Although most studies predicted that the proposed methods would work on pathological gait, less than one third were validated on such data. Clinical applications of gait detection algorithms were considered, and we recommend a combination of IMU and rule-based methods as an optimal solution.

The mental representation of the human gait in hip osteoarthrosis and total hip arthroplasty patients: A clinical cross-sectional study.

OBJECTIVE:: To explore differences in gait-specific long-term memory structures and actual gait performance between patients with hip osteoarthrosis, patients seen six months after total hip arthroplasty and healthy controls to gain insights into the role of the gait-specific mental representation for rehabilitation. DESIGN:: Cross-sectional study. SUBJECTS:: Twenty hip osteoarthrosis patients, 20 patients seen six months after total hip arthroplasty and 20 healthy controls. METHODS:: Spatio-temporal (gait speed, step length) and temporophasic (stance time, swing time, single support time, total double support time) gait parameters, and gait variability were measured with an electronic walkway (OptoGait). The gait-specific mental representation was assessed using the structural dimensional analysis of mental representations (SDA-M). RESULTS:: Hip osteoarthrosis patients showed significantly longer stance and total double support times, shorter swing and single support times, and a decreased gait speed as compared with healthy controls (all P < 0.01). The differences in double support times were still evident in patients seen six months after total hip arthroplasty ( P < 0.01). The gait-specific mental representation differed between hip osteoarthrosis patients and healthy controls with regard to mid-stance and mid-swing phases; the mid-stance phase was still affected six months after total hip arthroplasty (both P < 0.05). CONCLUSION:: Our data indicated that actual gait performance and gait-specific long-term memory structures differ between hip osteoarthrosis patients and healthy controls. Important, some of these disease-related changes were still evident in patients seen six months after total hip arthroplasty.

Turning Analysis during Standardized Test Using On-Shoe Wearable Sensors in Parkinson's Disease.

Mobile gait analysis systems using wearable sensors have the potential to analyze and monitor pathological gait in a finer scale than ever before. A closer look at gait in Parkinson's disease (PD) reveals that turning has its own characteristics and requires its own analysis. The goal of this paper is to present a system with on-shoe wearable sensors in order to analyze the abnormalities of turning in a standardized gait test for PD. We investigated turning abnormalities in a large cohort of 108 PD patients and 42 age-matched controls. We quantified turning through several spatio-temporal parameters. Analysis of turn-derived parameters revealed differences of turn-related gait impairment in relation to different disease stages and motor impairment. Our findings confirm and extend the results from previous studies and show the applicability of our system in turning analysis. Our system can provide insight into the turning in PD and be used as a complement for physicians' gait assessment and to monitor patients in their daily environment.

The mental representation of the human gait in patients with severe knee osteoarthrosis: a clinical study to aid understanding of impairment and disability.

OBJECTIVE: Objectives were (1) to explore differences in gait-specific long-term memory structures and gait performance between knee osteoarthrosis patients and healthy subjects and (2) to identify the extent to which the gait-specific mental representation is associated with gait performance. DESIGN: Cross-sectional study. SUBJECTS: In total, 18 knee osteoarthrosis patients and 18 control subjects. METHODS: Spatio-temporal (gait speed, step length) and temporophasic (stance time, swing time, single support time, total double support time) gait parameters and gait variability were measured with an electronic walkway (OptoGait). The mental representation was assessed using the structural dimensional analysis of mental representations (SDA-M). RESULTS: (1) Patients showed significantly longer stance times ( P < 0.002) and total double support times, shorter swing times and single support times, a decreased gait speed ( P-values < 0.001) and structural differences in the gait-specific mental representation as compared with the healthy controls. (2) Correlation analyses revealed the mental representation of the human gait to be associated with actual gait performance in osteoarthrosis patients. Double support times were positively associated with the structural quality of the mental representation and step length variability was positively associated with the number of sequencing errors in the representation. CONCLUSION: The gait-specific mental representation and actual gait performance differ between patients with severe knee osteoarthrosis and healthy controls, and both are linked to one another. This finding suggests that musculoskeletal disorders can lead to changes in the mental representation of the gait, and as such the SDA-M could provide useful information to improve the rehabilitation following osteoarthrosis.

The Traffic Light System: A user-friendly alternative for gait data representation.

BACKGROUND: Instrumented gait analysis is an established procedure in biomechanical assessment, requiring specially-trained analysts to interpret the complex graphical output generated. RESEARCH QUESTION: Does a new method of visual representation of lower limb kinematic gait analysis data provide a reliable and valid method of interpretation of biomechanical data for healthcare professionals? METHODS: An innovative system based on the Traffic Lights System (TLS) was developed. Simulated abnormal gait was captured using a 16-camera optoelectronic motion capture system, and the results were presented in both the Traditional Graphical System (TGS) format and the new TLS. An online form was filled by health professionals who attempted to interpret normal and abnormal motion in the joints presented in the 2 output formats. RESULTS: Out of 26 raters, 18 preferred the new system because of its user-friendliness and its ease of interpretation. 2 raters preferred the TGS, with one of these raters clarifying that the preference is due to colour blindness. For intra-rater reliability, 2 trained raters provided a second response for the TGS (Cronbach's Alpha ranging between 0.733 and 0.918), whilst the TLS resulted in Cronbach's Alpha between 0.817 and 1.00 amongst 3 untrained raters. The Fleiss Multi-rater Kappa Test demonstrated low inter-rater reliability amongst raters in the TGS, whereas the overall Fleiss Multi-rater Kappa values of the TLS surpassed the TGS in all 3 studies. SIGNIFICANCE: This study showed that whilst trained health professionals have high intra-rater reliability in interpreting traditional gait analysis results, those professionals inexperienced in the system, do not always comprehend the complex graphs generated by the system when presenting gait analysis data. When these graphs are transformed into coloured outputs representing the extent of the movement, the TLS has demonstrated high validity and high intra- and inter-rater reliability, significantly exceeding those of the TGS, especially in untrained health professionals.

The impact of Three-Dimensional Gait Analysis in adults with pathological gait on management recommendations.

BACKGROUND: Three-Dimensional Gait Analysis (3DGA) is a gold standard tool that can help identify pathological components of walking patterns. It has been well established that this tool influences the treatment decision making of clinicians treating paediatric patients with Cerebral Palsy, but it has not been established whether this tool changes decision making of clinicians treating adults with complex pathological gait. RESEARCH QUESTION: To investigate the impact of pre-treatment 3DGA on treatment plans and management of adults with complex pathological gait. METHOD: This retrospective audit examined the medical records of 87 patients undergoing pre-treatment 3DGA between 2014 and 2019. The review collected treatment plans from the initial referral, the post-gait analysis multidisciplinary report, and post-intervention progress notes with consistencies and differences noted throughout the care pathway. RESULTS: Treatment plans of patients were altered in 80 % (N = 32) of patients following 3DGA assessment and recommendations. These treatment plan alterations included a change in surgery or avoidance of surgery, changes in orthosis prescriptions, casting or rehabilitation; and administration or changes in administration of Botulinum Neurotoxin (BoNT-A). In 47 % (N = 15) of cases the change in plans represented a de-escalation in intervention requirements (e.g. BoNT-A in lieu of surgical intervention), and in 31 % (N = 10) the change in plans represented an escalation in intervention requirements (e.g. requirement for surgery). These changes in treatment plans were either fully or partly enacted by the referring consultant in 86 % of cases. SIGNIFICANCE: Pre-treatment 3DGA impacts the management of adult patients with complex pathological gait and facilitates patients potentially avoiding unnecessary interventions. Further investigation is needed to determine the cost effectiveness of 3DGA in this population and the impact of pre-treatment 3DGA on management outcomes.

Hybrid Deep Neural Network Framework Combining Skeleton and Gait Features for Pathological Gait Recognition.

Human skeleton data obtained using a depth camera have been used for pathological gait recognition to support doctor or physician diagnosis decisions. Most studies for skeleton-based pathological gait recognition have used either raw skeleton sequences directly or gait features, such as gait parameters and joint angles, extracted from raw skeleton sequences. We hypothesize that using skeleton, joint angles, and gait parameters together can improve recognition performance. This study aims to develop a deep neural network model that effectively combines different types of input data. We propose a hybrid deep neural network framework composed of a graph convolutional network, recurrent neural network, and artificial neural network to effectively encode skeleton sequences, joint angle sequences, and gait parameters, respectively. The features extracted from three different input data types are fused and fed into the final classification layer. We evaluate the proposed model on two different skeleton datasets (a simulated pathological gait dataset and a vestibular disorder gait dataset) that were collected using an Azure Kinect. The proposed model, with multiple types of input, improved the pathological gait recognition performance compared to single input models on both datasets. Furthermore, it achieved the best performance among the state-of-the-art models for skeleton-based action recognition.

Simple rule to automatically recognize the orientation of the sagittal plane foot angular velocity for gait analysis using IMUs on the feet of individuals with heterogeneous motor disabilities.

Automatic sensor-to-foot alignment is required in clinical gait analysis using inertial sensors to avoid assumptions about sensors initial positions and orientations. Numerous studies have proposed alignment methods. The current study aimed at describing and accessing the performance of a simple rule to automatically recognize the orientation of the sagittal plane foot angular velocity that can be used with any alignment method and any populations including individuals with severe motor disorders such as patients with cerebral palsy (CP). Fifty-five participants (15 healthy, 15 with CP and 25 with various other motor disorders) wore IMUs on both feet during one or several visits of clinical gait analysis (CGA) with optical motion capture system as reference. The foot coordinate system was determined using acceleration during motionless periods and angular velocity during walking, as previously described in the literature. Based on the foot sagittal plane angular velocity, a novel rule is introduced to determine the latest uncertainty related to mediolateral axis direction which often causes errors. It consisted of massively filtering the signal and applying a simple peak detection, omitting the double peaks with the same sign. The time between the negative and positive peaks can inform on the axis direction. This verification showed excellent results with 99,94% sensibility against the reference. This simple rule could be used to further improve existing sensor-to-segment algorithms with inertial sensors located on the feet, and thus improve pathological gait analysis.

Three decades of gait index development: A comparative review of clinical and research gait indices.

BACKGROUND: A wide variety of indices have been developed to quantify gait performance markers and associate them with their respective pathologies. Indices scores have enabled better decisions regarding patient treatments and allowed for optimized monitoring of the evolution of their condition. The extensive range of human gait indices presented over the last 30 years is evaluated and summarized in this narrative literature review exploring their application in clinical and research environments. METHODS: The analysis will explore historical and modern gait indices, focusing on the clinical efficacy with respect to their proposed pathology, age range, and associated parameter limits. Features, methods, and clinically acceptable errors are discussed while simultaneously assessing indices advantages and disadvantages. This review analyses all indices published between 1994 and February 2021 identified using the Medline, PubMed, ScienceDirect, CINAHL, EMBASE, and Google Scholar databases. FINDINGS: A total of 30 indices were identified as noteworthy for clinical and research purposes and another 137 works were included for discussion. The indices were divided in three major groups: observational (13), instrumented (16) and hybrid (1). The instrumented indices were further sub-divided in six groups, namely kinematic- (4), spatiotemporal- (5), kinetic- (2), kinematic- and kinetic- (2), electromyographic- (1) and Inertial Measurement Unit-based indices (2). INTERPRETATION: This work is one of the first reviews to summarize observational and instrumented gait indices, exploring their applicability in research and clinical contexts. The aim of this review is to assist members of these communities with the selection of the proper index for the group in analysis.

Dynamic gait stability in patients with idiopathic normal pressure hydrocephalus with high and low fall-risk.

BACKGROUND: This study aimed to investigate whether dynamic gait stability differs between idiopathic normal-pressure hydrocephalus with high- and low-fall-risk. METHODS: Participants comprised 40 idiopathic normal-pressure hydrocephalus patients and 23 healthy-controls. Idiopathic normal-pressure hydrocephalus patients were divided into those with high-fall-risk (n = 20) and low-fall-risk (n = 20) groups using the cut-off score of ≤14/30 for fall-risk on the Functional Gait Assessment. Dynamic stability during gait was assessed by three-dimensional motion analysis. Dynamic stability was defined as the ability to maintain an extrapolated center of mass within the base of support at heel contact, with the distance between the two defined as the margin of stability. Conscious motor control was assessed by the Movement-Specific Reinvestment Scale. FINDINGS: Anteroposterior and mediolateral margin of stabilities were significantly larger in both idiopathic normal-pressure hydrocephalus groups than in healthy-controls. The mediolateral margin of stability was significantly higher in the high-fall-risk group than in the low-fall-risk group; whereas, the anteroposterior margin of stability did not differ between idiopathic normal-pressure hydrocephalus groups. The Movement-Specific Reinvestment Scale was significantly higher in the high-fall-risk group than in the low-fall-risk group. INTERPRETATION: Idiopathic normal-pressure hydrocephalus patients with have high forward and lateral dynamic stability during gait regardless of their fall-risk. In particular, idiopathic normal-pressure hydrocephalus patients with high-fall-risk may consciously maintain lateral dynamic stability to a greater extent than those with low-fall-risk. These findings highlight a conscious motor control component in the pathological gait of idiopathic normal-pressure hydrocephalus, and provide clues for rehabilitation and fall prevention strategies in idiopathic normal-pressure hydrocephalus patients.

Impact of the Marker Set Configuration on the Accuracy of Gait Event Detection in Healthy and Pathological Subjects.

For interpreting outcomes of clinical gait analysis, an accurate estimation of gait events, such as initial contact (IC) and toe-off (TO), is essential. Numerous algorithms to automatically identify timing of gait events have been developed based on various marker set configurations as input. However, a systematic overview of the effect of the marker selection on the accuracy of estimating gait event timing is lacking. Therefore, we aim to evaluate (1) if the marker selection influences the accuracy of kinematic algorithms for estimating gait event timings and (2) what the best marker location is to ensure the highest event timing accuracy across various gait patterns. 104 individuals with cerebral palsy (16.0 ± 8.6 years) and 31 typically developing controls (age 20.6 ± 7.8) performed clinical gait analysis, and were divided into two out of eight groups based on the orientation of their foot, in sagittal and frontal plane at mid-stance. 3D marker trajectories of 11 foot/ankle markers were used to estimate the gait event timings (IC, TO) using five commonly used kinematic algorithms. Heatmaps, for IC and TO timing per group were created showing the median detection error, compared to detection using vertical ground reaction forces, for each marker. Our findings indicate that median detection errors can be kept within 7 ms for IC and 13 ms for TO when optimizing the choice of marker and detection algorithm toward foot orientation in midstance. Our results highlight that the use of markers located on the midfoot is robust for detecting gait events across different gait patterns.

Gait Assessment Using Three-Dimensional Acceleration of the Trunk in Idiopathic Normal Pressure Hydrocephalus.

Background: The subjective evaluation of pathological gait exhibits a low inter-rater reliability. Therefore, we developed a three-dimensional acceleration of the trunk during walking to assess the pathological gait quantitatively. Methods: We evaluated 97 patients who underwent the cerebrospinal tap test and were diagnosed with idiopathic normal pressure hydrocephalus (iNPH) and 68 healthy elderlies. The gait features of all patients were evaluated and classified as one of the following: freezing of gait, wide-based gait, short-stepped gait, shuffling gait, instability, gait festination, difficulty in changing direction, and balance disorder in standing up. All gait features of 68 healthy elderlies were treated as normal. Trunk acceleration was recorded automatically by a smartphone placed on the umbilicus during a 15-foot walking test. Two novel indices were created. The first index was a trunk acceleration index, which was defined as (forward acceleration fluctuation) + (vertical acceleration fluctuation) - (lateral acceleration fluctuation) based on the multivariate logistics regression model, and the second index was created by multiplying the forward acceleration with the vertical acceleration. Additionally, 95% confidence ellipsoid volume of the three-dimensional accelerations was assessed. Results: Forward and vertical acceleration fluctuations were significantly associated with the probability of an iNPH-specific pathological gait. The trunk acceleration index demonstrated the strongest association with the probability of an iNPH-specific pathological gait. The areas under the receiver-operating characteristic curves for detecting 100% probability of an iNPH-specific pathological gait were 86.9% for forward acceleration fluctuation, 88.0% for vertical acceleration fluctuation, 82.8% for lateral acceleration fluctuation, 89.0% for trunk acceleration index, 88.8% for forward × vertical acceleration fluctuation, and 87.8% for 95% confidence ellipsoid volume of the three-dimensional accelerations. Conclusions: The probability of a pathological gait specific to iNPH is high at the trunk acceleration fluctuation, reduced in the forward and vertical directions, and increased in the lateral direction.

A review of gait disorders in the elderly and neurological patients for robot-assisted training.

Purpose: Ambulation is an important objective for people with pathological gaits. Exoskeleton robots can assist these people to complete their activities of daily living. There are exoskeletons that have been presented in literature to assist the elderly and other pathological gait users. This article presents a review of the degree of support required in the elderly and neurological gait disorders found in the human population. This will help to advance the design of robot-assisted devices based on the needs of the end users.Methods: The articles included in this review are collected from different databases including Science Direct, Springer Link, Web of Science, Medline and PubMed and with the purpose to investigate the gait parameters of elderly and neurological patients. Studies were included after considering the full texts and only those which focus on spatiotemporal, kinematic and kinetic gait parameters were selected as they are most relevant to the scope of this review. A systematic review and meta-analysis were conducted.Results: The meta-analysis report on the spatiotemporal, kinematic and kinetic gait parameters of elderly and neurological patients revealed a significant difference based on the type and level of impairment. Healthy elderly population showed deviations in the gait parameters due to age, however, significant difference is observed in the gait parameters of the neurological patients.Conclusion: A level of agreement was observed in most of the studies however the review also noticed some controversies among different studies in the same group. The review on the spatiotemporal, kinematics and kinetic gait parameters will provide a summary of the fundamental needs of the users for the future design and development of robotic assistive devices.Implications for rehabilitationThe support requirements provide the foundation for designing assistive devices.The findings will be crucial in defining the design criteria for robot assistive devices.