Trajectory of Walking Function in Late-Stage Older Individuals Managed with a Regular Exercise Program: A 5-Year Longitudinal Tracking with an IoT Gait Analysis System Using Accelerometers

Purpose: This study focused on maintaining and improving the walking function of late-stage older individuals while longitudinally tracking the effects of regular exercise programs in a day-care service specialized for preventive care over 5 years, using detailed gait function measurements with an accelerometer-based system. Methods: Seventy individuals (17 male and 53 female) of a daycare service in Tokyo participated in a weekly exercise program, meeting 1 - 2 times. The average age of the participants at the start of the program was 81.4 years. Gait function, including gait speed, stride length, root mean square (RMS) of acceleration, gait cycle time and its standard deviation, and left-right difference in stance time, was evaluated every 6 months. Results: Gait speed and stride length improved considerably within six months of starting the exercise program, confirming an initial improvement in gait function. This suggests that regular exercise programs can maintain or improve gait function even age groups that predictably have a gradual decline in gait ability due to enhanced age. In the long term, many indicators tended to approach baseline values. However, the exercise program seemingly counteracts age-related changes in gait function and maintains a certain level of function. Conclusions: While a decline in gait ability with aging is inevitable, establishing appropriate exercise habits in late-stage older individuals may contribute to long-term maintenance of gait function.

The Immediate Analgesic Effect and Impact on Gait Function of Transcutaneous Electrical Nerve Stimulation in Late-Stage Elderly Individuals with Knee Pain: Examination of Gait Function Using an IoT-Based Gait Analysis Device

Purpose: This study verified the effects of transcutaneous electrical nerve stimulation (TENS), which can be worn during walking and exercise, in elderly individuals with late-stage knee pain who exercise regularly. Methods: Thirty-two late-stage elderly individuals were evaluated for knee pain during rest, walking, and program exercises, with and without TENS. Gait analysis was performed using an IoT-based gait analysis device to examine the effects of TENS-induced analgesia on gait. Results: TENS significantly reduced knee pain during rest, walking, and programmed exercises, with the greatest analgesic effect observed during walking. The greater the knee pain without TENS, the more significant the analgesic effect of TENS. A comparison of gait parameters revealed a significant difference only in the gait cycle time, with a trend towards faster walking with TENS;however, the effect was limited. Conclusion: TENS effectively relieves knee pain in late-stage elderly individuals and can be safely applied during exercise. Pain management using TENS provides important insights into the implementation of exercise therapy in this age group.

Assessment of hindlimb motor recovery affer severe thoracic spinal cord injury in rats: classification of CatWalk XT■ gait analysis parameters

Assessment of locomotion recovery in preclinical studies of experimental spinal cord injury remains challenging. We studied the CatWalk XT■gait analysis for evaluating hindlimb functional recovery in a widely used and clinically relevant thoracic contusion/compression spinal cord injury model in rats. Rats were randomly assigned to either a T9 spinal cord injury or sham laminectomy. Locomotion recovery was assessed using the Basso, Beattie, and Bresnahan open field rating scale and the CatWalk XT■gait analysis. To determine the potential bias from weight changes, corrected hindlimb(H) values(divided by the unaffected forelimb(F) values) were calculated. Six weeks after injury, cyst formation, astrogliosis, and the deposition of chondroitin sulfate glycosaminoglycans were assessed by immunohistochemistry staining. Compared with the baseline, a significant spontaneous recovery could be observed in the CatWalk XT■parameters max intensity, mean intensity, max intensity at%, and max contact mean intensity from 4 weeks after injury onwards. Of note, corrected values(H/F) of CatWalk XT■parameters showed a significantly less vulnerability to the weight changes than absolute values, specifically in static parameters. The corrected CatWalk XT■parameters were positively correlated with the Basso, Beattie, and Bresnahan rating scale scores, cyst formation, the immunointensity of astrogliosis and chondroitin sulfate glycosaminoglycan deposition. The CatWalk XT■gait analysis and especially its static parameters, therefore, seem to be highly useful in assessing spontaneous recovery of hindlimb function after severe thoracic spinal cord injury. Because many CatWalk XT■parameters of the hindlimbs seem to be affected by body weight changes, using their corrected values might be a valuable option to improve this dependency.

Efficient Gait Analysis Using Deep Learning Techniques

Human Activity Recognition(HAR)has always been a difficult task to tackle.It is mainly used in security surveillance,human-computer interaction,and health care as an assistive or diagnostic technology in combination with other technologies such as the Internet of Things(IoT).Human Activity Recognition data can be recorded with the help of sensors,images,or smartphones.Recognizing daily routine-based human activities such as walking,standing,sitting,etc.,could be a difficult statistical task to classify into categories and hence 2-dimensional Convolutional Neural Network(2D CNN)MODEL,Long Short Term Memory(LSTM)Model,Bidirectional long short-term memory(Bi-LSTM)are used for the classification.It has been demonstrated that recognizing the daily routine-based on human activities can be extremely accurate,with almost all activities accurately getting recognized over 90%of the time.Furthermore,because all the examples are generated from only 20 s of data,these actions can be recognised fast.Apart from classification,the work extended to verify and investigate the need for wearable sensing devices in individually walking patients with Cerebral Palsy(CP)for the evaluation of chosen Spatio-temporal features based on 3D foot trajectory.Case-control research was conducted with 35 persons with CP ranging in weight from 25 to 65 kg.Optical Motion Capture(OMC)equipment was used as the referral method to assess the functionality and quality of the foot-worn device.The average accuracy±precision for stride length,cadence,and step length was 3.5±4.3,4.1±3.8,and 0.6±2.7 cm respectively.For cadence,stride length,swing,and step length,people with CP had considerably high inter-stride variables.Foot-worn sensing devices made it easier to examine Gait Spatio-temporal data even without a laboratory set up with high accuracy and precision about gait abnormalities in people who have CP during linear walking.

Gait analysis in swine,sheep,and goats after neurologic injury:a literature review

Medical research on neurologic ailments requires representative animal models to validate treatments before they are translated to human clinical trials.Rodents are the predominant animal model used in neurological research despite limited anatomic and physiologic similarities to humans.As a result,functional testing designed to assess locomotor recovery after neurologic impairment is well established in rodent models.Comparatively,large r,more clinically relevant models have not been as well studied.To achieve similar locomotor testing standardization in larger animals,the models must be accessible to a wide array of researchers.Non-human primates are the most relevant animal model fo r translational research,however ethical and financial barriers limit their accessibility.This review focuses on swine,sheep,and goats as large animal alternatives for transitional studies between rodents and non-human primates.The objective of this review is to compare motor testing and data collection methods used in swine,sheep,and goats to encourage testing standardization in these larger animal models.The PubMed database was analyzed by searching combinations of swine,sheep,and goats,neurologic injuries,and functional assessments.Findings were categorized by animal model,data collection method,and assessment design.Swine and sheep were used in the majority of the studies,while only two studies were found using goats.The functional assessments included open pen analysis,treadmill walking,and guided free walking.Data collection methods included subjective behavioral rating scales and objective tools such as pressure-sensitive mats and image-based analysis software.Overall,swine and sheep were well-suited for a variety of assessment designs,with treadmill walking and guided free walking offering the most consistency across multiple trials.Data collection methods varied,but image-based gait analysis software provided the most robust analysis.Future studies should be conducted to standardize functional testing methods after neurologic impairment in large animals.

Gait Analysis of a Subject with Tarsometatarsal Prosthesis

Introduction: Gait analysis of an adult man after trans-metatarsal unilateral amputation is described. Objective: Instrumental gait analysis of a subject 15 years after trans-metatarsal unilateral amputation in two footwear conditions: while walking barefoot and with prosthesis. Materials and Methods: In a movement analysis laboratory, locomotion studies were carried out at freely chosen walking speed by a 65-year-old subject, obtaining kinematic, kinetic and surface electromyographic data in time and space. Gait analysis was performed using instrumental technologies from a digital eco-system applying walking protocols. Results: When the patient wore the prosthesis, several positive improvements were observed in various aspects of gait. These included enhancements in the base of support, gait speed, and joint range of movements. Additionally, there was a slight improvement in the vertical ground reaction forces pattern, indicating a positive effect of the assistive technology. Furthermore, the use of the prosthesis led to a more organized pattern of muscle activity, which further supports its beneficial impact. However, it is worth noting that some challenges still persisted, particularly regarding stabilizing the body during the double support phase. Despite this difficulty, the overall findings suggest that the use of the prosthesis offers valuable improvements to the patient’s gait dynamics. Conclusions: After conducting a thorough analysis of the parameters related to the gait of a subject who underwent a trans-metatarsal unilateral amputation fifteen years ago, it was found that the use of prosthesis had a positive impact. This study demonstrated important improvements in some kinematic and kinetic parameters, including muscle activation patterns, indicating an increase in comfort and confidence while utilizing the prosthetic device.

Sensor-Based Gait Analysis for Parkinson’s Disease Prediction

Parkinson’s disease is identified as one of the key neurodegenerative disorders occurring due to the damages present in the central nervous system.The cause of such brain damage seems to be fully explained in many research studies,but the understanding of its functionality remains to be impractical.Specifically,the development of a quantitative disease prediction model has evolved in recent decades.Moreover,accelerometer sensor-based gait analysis is accepted as an important tool for recognizing the walking behavior of the patients during the early prediction and diagnosis of Parkinson’s disease.This type of minimal infrastructure equipment helps in analyzing the Parkinson’s gait properties without affecting the common behavioral patterns during the clinical practices.Therefore,the Accelerometer Sensor-based Parkinson’s Disease Identi-fication System(ASPDIS)is introduced with a kernel-based support vector machine classifier model to make an early prediction of the disease.consequently,the proposed classifier can easily predict various severity levels of Parkinson’s disease from the sensor data.The performance of the proposed classifier is com-pared against the existing models such as random forest,decision tree,and k-near-est neighbor classifiers respectively.As per the experimental observation,the proposed classifier has more capability to differentiate Parkinson’s from non-Parkinson patients depending upon the severity levels.Also,it is found that the model has outperformed the existing classifiers concerning prediction time and accuracy respectively.

Computer-aided texture analysis combined with experts' knowledge: Improving endoscopic celiac disease diagnosis

AIM: To further improve the endoscopic detection of intestinal mucosa alterations due to celiac disease(CD).METHODS: We assessed a hybrid approach based on the integration of expert knowledge into the computerbased classification pipeline. A total of 2835 endoscopic images from the duodenum were recorded in 290 children using the modified immersion technique(MIT). These children underwent routine upper endoscopy for suspected CD or non-celiac upper abdominal symptoms between August 2008 and December 2014. Blinded to the clinical data and biopsy results, three medical experts visually classified each image as normal mucosa(Marsh-0) or villous atrophy(Marsh-3). The experts' decisions were further integrated into state-of-the-arttexture recognition systems. Using the biopsy results as the reference standard, the classification accuracies of this hybrid approach were compared to the experts' diagnoses in 27 different settings.RESULTS: Compared to the experts' diagnoses, in 24 of 27 classification settings(consisting of three imaging modalities, three endoscopists and three classification approaches), the best overall classification accuracies were obtained with the new hybrid approach. In 17 of 24 classification settings, the improvements achieved with the hybrid approach were statistically significant(P < 0.05). Using the hybrid approach classification accuracies between 94% and 100% were obtained. Whereas the improvements are only moderate in the case of the most experienced expert, the results of the less experienced expert could be improved significantly in 17 out of 18 classification settings. Furthermore, the lowest classification accuracy, based on the combination of one database and one specific expert, could be improved from 80% to 95%(P < 0.001).CONCLUSION: The overall classification performance of medical experts, especially less experienced experts, can be boosted significantly by integrating expert knowledge into computer-aided diagnosis systems.

Regionalization of epididymal duct and epithelium in rats and mice by automatic computer-aided morphometric analysis

Aim： To establish a rat and mouse epididymal map based on the use of the Epiquatre automatic software for histologic image analysis. Methods： Epididymides from five adult rats and five adult mice were fixed in alcoholic Bouin＇s fixative and embedded in paraffin. Serial longitudinal sections through the medial aspect of the organ were cut at 10 jam and stained with hematoxylin and eosin. As determined from major connective tissue septa, nine subdivisions of the rat epididymis and seven for the mouse were determined, consisting of five sub-regions in the caput （rat and mouse）, one （mouse） or three （rat） in the corpus and one in the cauda （rat and mouse）. Using the Epiquatre software, several tubular, luminal and epithelial morphometric parameters were evaluated. Results： Statistical comparison of the quantitative parameters revealed regional differences （2-5 in the rat, 3-6 in the mouse, dependent on parameters） with caput regions 1 and 2 being largely distinguishable from the similar remaining caput and corpus, which were in turn recognizable from the cauda regions in both species. Conclusion： The use of the Epiquatre software allowed us to establish regression curves for different morphometric parameters that can permit the detection of changes in their values under different pathological or experimental conditions. （Asian J Androl 2005 Sep; 7： 267-275）

Gait analysis of children with spastic hemiplegic cerebral palsy

An experiment was carried out in the key laboratory for Technique Diagnosis and Function Assessment of Winter Sports of China to investigate the differences in gait characteristics between healthy children and children with spastic hemiplegic cerebral palsy. With permission of their parents, 200 healthy children aged 3 to 6 years in the kindergarten of Northeastern University were enrolled in this experiment. Twenty children aged 3 to 6 years with spastic hemiplegic cerebral palsy from Shengjing Hospital, China were also enrolled in this experiment. Standard data were collected by simultaneously recording gait information from two digital cameras. DVracker was used to analyze the standard data. The children with hemiplegic cerebra palsy had a longer gait cycle, slower walking speed, and longer support phase than did the healthy children. The support phase was longer than the swing phase in the children with hemiplegic cerebral palsy. There were significant differences in the angles of the hip, knee, and ankle joint between children with cerebral palsy and healthy children at the moment of touching the ground and buffering, and during pedal extension. Children with hemiplegic cerebral palsy had poor motor coordination during walking, which basically resulted in a short stride, high stride frequency to maintain speed, more obvious swing, and poor stability.

View-invariant Gait Authentication Based on Silhouette Contours Analysis and View Estimation

In this paper, we propose a novel view-invariant gait authentication method based on silhouette contours analysis and view estimation. The approach extracts Lucas-Kanade based gait flow image and head and shoulder mean shape(LKGFI-HSMS)of a human by using the Lucas-Kanade s method and procrustes shape analysis(PSA). LKGFI-HSMS can preserve the dynamic and static features of a gait sequence. The view between a person and a camera is identified for selecting the target s gait feature to overcome view variations. The similarity scores of LKGFI and HSMS are calculated. The product rule combines the two similarity scores to further improve the discrimination power of extracted features. Experimental results demonstrate that the proposed approach is robust to view variations and has a high authentication rate.

Spinal alignment evolution with age:A prospective gait analysis study

AIM To describe,using gait analysis,the development of spinal motion in the growing child.METHODS Thirty-six healthy children aged from 3 to 16 years old were included in this study for a gait analysis(9m-walk).Various kinematic parameters were recorded and analyzed such as thoracic angle(TA),lumbar angle(LA)and sagittal vertical axis(SVA).The kinetic parameters were the net reaction moments(N.m/kg)at the thoracolumbar and lumbosacral junctions.RESULTS TA and LA curves were not statistically correlated to the age(respectively,P=0.32 and P=0.41).SVA increased significantly with age(P<0.001).Moments in sagittal plane at the lumbosacral junction were statistically correlated to the age(P=0.003),underlining the fact that sagittal mechanical constraints at the lumbosacral junction increase with age.Moments in transversal plane at the thoracolumbar and lumbosacral junctions were statistically correlated to the age(P=0.0002and P=0.0006),revealing that transversal mechanical constraints decrease with age.CONCLUSION The kinetic analysis showed that during growth,a decrease of torsional constraint occurs while an increase of sagittal constraint is observed.These changes in spine biomechanics are related to the crucial role of the trunk for bipedalism acquisition,allowing stabilization despite lower limbs immaturity.With the acquisition of mature gait,the spine will mainly undergo constraints in the sagittal plane.

An Efficient and Robust Fall Detection System Using Wireless Gait Analysis Sensor with Artificial Neural Network (ANN) and Support Vector Machine (SVM) Algorithms

In this work, a total of 322 tests were taken on young volunteers by performing 10 different falls, 6 different Activities of Daily Living (ADL) and 7 Dynamic Gait Index (DGI) tests using a custom-designed Wireless Gait Analysis Sensor (WGAS). In order to perform automatic fall detection, we used Back Propagation Artificial Neural Network (BP-ANN) and Support Vector Machine (SVM) based on the 6 features extracted from the raw data. The WGAS, which includes a tri-axial accelerometer, 2 gyroscopes, and a MSP430 microcontroller, is worn by the subjects at either T4 (at back) or as a belt-clip in front of the waist during the various tests. The raw data is wirelessly transmitted from the WGAS to a near-by PC for real-time fall classification. The BP ANN is optimized by varying the training, testing and validation data sets and training the network with different learning schemes. SVM is optimized by using three different kernels and selecting the kernel for best classification rate. The overall accuracy of BP ANN is obtained as 98.20% with LM and RPROP training from the T4 data, while from the data taken at the belt, we achieved 98.70% with LM and SCG learning. The overall accuracy using SVM was 98.80% and 98.71% with RBF kernel from the T4 and belt position data, respectively.

Examining Gait Patterns after Total Knee Arthroplasty Using Parameterization and Principal Component Analysis

The use of parameterization in assessing gait waveforms has been widely accepted, although it is recognized that this approach excludes the majority of information contained in the waveform. Waveform analysis techniques, such as principal component analysis (PCA), have gained popularity in recent years as a more effective approach to extracting important information from human movement waveforms, but are more challenging to interpret. Few studies have compared these two different approaches to determine which yields the most relevant information. This study compared the kinematic patterns during gait of six total knee arthroplasty (TKA) subjects (10 TKA knees), to a group of 10 age-matched asymptomatic control subjects (19 control knees). An eight-camera Vicon M-cam system was used to track movement and compute joint angles. Group differences in parameterization (max and min peaks) values and principal component scores were tested using one-way ANOVA and Kruskal-Wallis tests. Using parameterization, the TKA group was characterized by reduced hip extension, increased hip flexion, increased anterior pelvic tilt, increased trunk tilt, and reduced sagittal ankle angles compared to the control group. Waveform analysis, by means of PCA, showed-magnitude shifts in sagittal ankle waveforms between groups, rather than solely reporting differences in peaks. Waveform analysis also indicated a significant shift in the magnitude of the entire waveform for hip angles, pelvic tilt, and trunk tilt, indicating no change in range of motion between groups, but rather a change in the way in which range of motion is achieved at the hip. This study has identified several gait variables that were significantly different between the TKA and control groups. Our results suggest that waveform analysis is effective at identifying magnitude shifts as sources of variability between groups, which would not necessarily be analyzed using conventional parameterization techniques unless one knew a priori where the variability would exist.

Task-Specific Gait Analysis: Faller versus Non-Faller Comparative Study

In this paper, a comparative analysis of walking patterns during different cognitive states is conducted, followed by the classification of our database into Fallers and Non-fallers;by Fallers we describe subjects with repeated falling history. Vertical Ground Reaction Forces (VGRF) acquired from underneath the heel and toes of both feet are processed and analyzed for that endeavor. The subjects underwent three levels of tasks: 1) Single task: Walking at self-selected-speed (MS), 2) Dual task: Walking while performing a verbal fluency task (MF) and 3) Complex Dual task: Walking while counting backwards (MD).The ultimate objective of our research is fall prediction among the elderly by characterizing the variation of time-domain feature of Gait signals. For that, walking VGRF is analyzed and tested for the existence of indicators of the effect of dual task on subject falling susceptibility, whether parametric or pattern-wise analysis. As a result to our work, dual task in Fallers VGRF signals were recognized at 74% while at those non-fallers were recognized at 85%. Most importantly, subjects with history of fall have shown more potential to change the way they walk while performing mathematical cognitive task.

Effect of Motor Relearning Combined with t DCS on Motor Function of Lower Extremities and Gait in Patients with Cerebral Infarction and 3D Gait Analysis

[Objectives]To observe the effect of motor relearning combined with transcranial direct current stimulation on the motor function of lower extremities in patients with cerebral infarction,and to observe its effect on gait by 3D gait analysis.[Methods]60 patients with cerebral infarction who met the inclusion criteria were randomly divided into 3 groups according to the order of treatment(n=20).Group A received motor relearning treatment,group B received transcranial direct current stimulation treatment,group C received motor relearning combined with transcranial direct current stimulation,and the curative effect was observed after 5 courses of treatment.[Results]Before treatment,FMA,MBI,spatio-temporal parameters for 3D gait analysis(gait frequency,gait cycle,stride length,gait speed,stride length deviation,double support)and lower limb joint motion parameters(affected side stride length,maximum hip flexion,maximum hip extension,maximum knee flexion,maximum knee extension,stance phase,swing phase)were compared among the three groups.After treatment,the FMA and MBI of the three groups increased,and the spatio-temporal parameters for 3D gait analysis(gait frequency,gait cycle,gait speed,double support)and the lower limb joint motion parameters(affected side stride length,maximum hip flexion,maximum hip extension,maximum knee flexion,swing phase)were all improved,while the spatio-temporal parameters(stride length and stride length deviation)and the lower limb joint motion parameters(maximum knee extension and stance phase)decreased.Compared with those before treatment,there were significant differences among the three groups(P<0.05).Through the comparison between groups,it was found that the FMA,MBI,spatio-temporal parameters for 3D gait analysis(gait frequency,gait cycle,gait speed,double support)and lower limb joint motion parameters(affected side stride length,maximum hip flexion,maximum hip extension,maximum knee flexion,swing phase)in group C were significantly higher than those in group A and B,while the spatio-temporal parameters(stride length and stride length deviation)and lower limb joint motion parameters(maximum knee extension and stance phase)in group C were significantly lower than those in group A and group B,and the difference was statistically significant(P<0.05).[Conclusions]Motor relearning combined with transcranial direct current stimulation could increase MBI and FMA,improve gait spatio-temporal parameters and lower limb joint motion parameters,and correct abnormal gait in patients with cerebral infarction.

Method for assessing the similarity and distance between curves/trials and its applications in gait analysis

In clinical assessment or sports exercise,it is common that a subject is required to repeat a specific per- formance so that a stable movement pattern is obtained and analysed.In practice,however,the trials done by a sub- ject vary more or less,depending on the psychological or physical conditions.Some of the trials can be used as rep- resentatives of the stable movement pattern,and some not.Therefore,there is a demand for a new method to identify which trials/curves are similar.The traditional methods used to assess curve similarity are not perfectly suitable for the case where there are only a few of trials available.This study proposes a similarity-distance coefficient to assess the similarity of curves/trials.A group of designed curves are used to validate the coefficient.The results show that given joint kinematic data during gait as examples,the proposed coefficient can be used to quantitatively evaluate the similarity of trials,and thus find which trials would be representative (s) for the gait investigated.The proposed method could be applied in various situations where repeat movements have to be measured and analysed.

Kinematic Analysis and Experimental Verification on the Locomotion of Gecko

This paper presents a kinematic analysis of the locomotion of a gecko,and experimental verification of the kinematic model.Kinematic analysis is important for parameter design,dynamic analysis,and optimization in biomimetic robot research. The proposed kinematic analysis can simulate,without iteration,the locomotion of gecko satisfying the constraint conditions that maintain the position of the contacted feet on the surface.So the method has an advantage for analyzing the climbing motion of the quadruped mechanism in a real time application.The kinematic model of a gecko consists of four legs based on 7-degrees of freedom spherical-revolute-spherical joints and two revolute joints in the waist.The motion of the kinematic model is simulated based on measurement data of each joint.The motion of the kinematic model simulates the investigated real gecko's motion by using the experimental results.The analysis solves the forward kinematics by considering the model as a combination of closed and open serial mechanisms under the condition that maintains the contact positions of the attached feet on the ground. The motions of each joint are validated by comparing with the experimental results.In addition to the measured gait,three other gaits are simulated based on the kinematic model.The maximum strides of each gait are calculated by workspace analysis.The result can be used in biomimetic robot design and motion planning.