Effects of multidisciplinary inpatient rehabilitation on everyday life physical activity and gait in patients with multiple sclerosis.

BACKGROUND: Multiple sclerosis is a progressive neurological disease that affects the central nervous system, resulting in various symptoms. Among these, impaired mobility and fatigue stand out as the most prevalent. The progressive worsening of symptoms adversely alters quality of life, social interactions and participation in activities of daily living. The main objective of this study is to bring new insights into the impact of a multidisciplinary inpatient rehabilitation on supervised walking tests, physical activity (PA) behavior and everyday gait patterns. METHODS: A total of 52 patients, diagnosed with multiple sclerosis, were evaluated before and after 3 weeks of inpatient rehabilitation. Each measurement period consisted of clinical assessments and 7 days home monitoring using foot-mounted sensors. In addition, we considered two subgroups based on the Expanded Disability Status Scale (EDSS) scores: 'mild' (EDSS < 5) and 'severe' (EDSS ≥ 5) disability levels. RESULTS: Significant improvements in fatigue, quality of life and perceived mobility were reported. In addition, walking capacity, as assessed by the 10-m walking test, two-minute walk test and timed-up-and-go test, improved significantly after rehabilitation. Regarding the home assessment, mildly disabled patients significantly increased their locomotion per day and complexity of daily PA pattern after rehabilitation, while severely disabled patients did not significantly change. There were distinct and significant differences in gait metrics (i.e., gait speed, stride length, cadence) between mildly and severely disabled patients, but the statistical models did not show a significant overall rehabilitation effect on these gait metrics. CONCLUSION: Inpatient rehabilitation showed beneficial effects on self-reported mobility, self-rated health questionnaires, and walking capacity in both mildly and severely disabled patients. However, these improvements do not necessarily translate to home performance in severely disabled patients, or only marginally in mildly disabled patients. Motivational and behavioral factors should also be considered and incorporated into treatment strategies.

Vertical and Leg Stiffness Modeling During Running: Effect of Speed and Incline.

A spring mass model is often used to describe human running, allowing to understand the concept of elastic energy storage and restitution. The stiffness of the spring is a key parameter and different methods have been developed to estimate both the vertical and the leg stiffness components. Nevertheless, the validity and the range of application of these models are still debated. The aim of the present study was to compare three methods (i. e., Temporal, Kinetic and Kinematic-Kinetic) of stiffness determination. Twenty-nine healthy participants equipped with reflective markers performed 5-min running bouts at four running speeds and eight inclines on an instrumented treadmill surrounded by a tri-dimensional motion camera system. The three methods provided valid results among the different speeds, but the reference method (i. e., Kinematic-Kinetic) provided higher vertical stiffness and lower leg stiffness than the two other methods (both p<0.001). On inclined terrain, the method using temporal parameters provided non valid outcomes and should not be used. Finally, this study highlights that both the assumption of symmetry between compression and decompression phases or the estimation of the vertical displacement and changes in leg length are the major sources of errors when comparing different speeds or different slopes.

Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium.

BACKGROUND: Although digital mobility outcomes (DMOs) can be readily calculated from real-world data collected with wearable devices and ad-hoc algorithms, technical validation is still required. The aim of this paper is to comparatively assess and validate DMOs estimated using real-world gait data from six different cohorts, focusing on gait sequence detection, foot initial contact detection (ICD), cadence (CAD) and stride length (SL) estimates. METHODS: Twenty healthy older adults, 20 people with Parkinson's disease, 20 with multiple sclerosis, 19 with proximal femoral fracture, 17 with chronic obstructive pulmonary disease and 12 with congestive heart failure were monitored for 2.5 h in the real-world, using a single wearable device worn on the lower back. A reference system combining inertial modules with distance sensors and pressure insoles was used for comparison of DMOs from the single wearable device. We assessed and validated three algorithms for gait sequence detection, four for ICD, three for CAD and four for SL by concurrently comparing their performances (e.g., accuracy, specificity, sensitivity, absolute and relative errors). Additionally, the effects of walking bout (WB) speed and duration on algorithm performance were investigated. RESULTS: We identified two cohort-specific top performing algorithms for gait sequence detection and CAD, and a single best for ICD and SL. Best gait sequence detection algorithms showed good performances (sensitivity > 0.73, positive predictive values > 0.75, specificity > 0.95, accuracy > 0.94). ICD and CAD algorithms presented excellent results, with sensitivity > 0.79, positive predictive values > 0.89 and relative errors < 11% for ICD and < 8.5% for CAD. The best identified SL algorithm showed lower performances than other DMOs (absolute error < 0.21 m). Lower performances across all DMOs were found for the cohort with most severe gait impairments (proximal femoral fracture). Algorithms' performances were lower for short walking bouts; slower gait speeds (< 0.5 m/s) resulted in reduced performance of the CAD and SL algorithms. CONCLUSIONS: Overall, the identified algorithms enabled a robust estimation of key DMOs. Our findings showed that the choice of algorithm for estimation of gait sequence detection and CAD should be cohort-specific (e.g., slow walkers and with gait impairments). Short walking bout length and slow walking speed worsened algorithms' performances. Trial registration ISRCTN - 12246987.

Automatic Body Segment and Side Recognition of an Inertial Measurement Unit Sensor during Gait.

Inertial measurement unit (IMU) sensors are widely used for motion analysis in sports and rehabilitation. The attachment of IMU sensors to predefined body segments and sides (left/right) is complex, time-consuming, and error-prone. Methods for solving the IMU-2-segment (I2S) pairing work properly only for a limited range of gait speeds or require a similar sensor configuration. Our goal was to propose an algorithm that works over a wide range of gait speeds with different sensor configurations while being robust to footwear type and generalizable to pathologic gait patterns. Eight IMU sensors were attached to both feet, shanks, thighs, sacrum, and trunk, and 12 healthy subjects (training dataset) and 22 patients (test dataset) with medial compartment knee osteoarthritis walked at different speeds with/without insole. First, the mean stride time was estimated and IMU signals were scaled. Using a decision tree, the body segment was recognized, followed by the side of the lower limb sensor. The accuracy and precision of the whole algorithm were 99.7% and 99.0%, respectively, for gait speeds ranging from 0.5 to 2.2 m/s. In conclusion, the proposed algorithm was robust to gait speed and footwear type and can be widely used for different sensor configurations.

Instrumented 5-Time Sit-To-Stand Test: Parameters Predicting Serious Falls beyond the Duration of the Test.

BACKGROUND: Falls are a major cause of injuries in older adults. To evaluate the risk of falls in older adults, clinical assessments such as the 5-time sit-to-stand (5xSTS) test can be performed. The development of inertial measurement units (IMUs) has provided the possibility of a more in-depth analysis of the movements' biomechanical characteristics during this test. The goal of the present study was to investigate whether an instrumented 5xSTS test provides additional information to predict multiple or serious falls compared to the conventional stopwatch-based method. METHODS: Data from 458 community-dwelling older adults were analyzed. The participants were equipped with an IMU on the trunk to extract temporal, kinematic, kinetic, and smoothness movement parameters in addition to the total duration of the test by the stopwatch. RESULTS: The total duration of the test obtained by the IMU and the stopwatch was in excellent agreement (Pearson's correlation coefficient: 0.99), while the total duration obtained by the IMU was systematically 0.52 s longer than the stopwatch. In multivariable analyses that adjusted for potential confounders, fallers had slower vertical velocity, reduced vertical acceleration, lower vertical power, and lower vertical jerk than nonfallers. In contrast, the total duration of the test measured by either the IMU or the stopwatch did not differ between the 2 groups. CONCLUSIONS: An instrumented 5xSTS test provides additional information that better discriminates among older adults those at risk of multiple or serious falls than the conventional stopwatch-based assessment.

Design and validation of a multi-task, multi-context protocol for real-world gait simulation.

BACKGROUND: Measuring mobility in daily life entails dealing with confounding factors arising from multiple sources, including pathological characteristics, patient specific walking strategies, environment/context, and purpose of the task. The primary aim of this study is to propose and validate a protocol for simulating real-world gait accounting for all these factors within a single set of observations, while ensuring minimisation of participant burden and safety. METHODS: The protocol included eight motor tasks at varying speed, incline/steps, surface, path shape, cognitive demand, and included postures that may abruptly alter the participants' strategy of walking. It was deployed in a convenience sample of 108 participants recruited from six cohorts that included older healthy adults (HA) and participants with potentially altered mobility due to Parkinson's disease (PD), multiple sclerosis (MS), proximal femoral fracture (PFF), chronic obstructive pulmonary disease (COPD) or congestive heart failure (CHF). A novelty introduced in the protocol was the tiered approach to increase difficulty both within the same task (e.g., by allowing use of aids or armrests) and across tasks. RESULTS: The protocol proved to be safe and feasible (all participants could complete it and no adverse events were recorded) and the addition of the more complex tasks allowed a much greater spread in walking speeds to be achieved compared to standard straight walking trials. Furthermore, it allowed a representation of a variety of daily life relevant mobility aspects and can therefore be used for the validation of monitoring devices used in real life. CONCLUSIONS: The protocol allowed for measuring gait in a variety of pathological conditions suggests that it can also be used to detect changes in gait due to, for example, the onset or progression of a disease, or due to therapy. TRIAL REGISTRATION: ISRCTN-12246987.

SmartSwim, a Novel IMU-Based Coaching Assistance.

Swimming coaches provide regular timed and technical feedback to swimmers and guide them efficiently in training sessions. Due to the complexity of swimmers' performance, which is not visible in qualitative observation, quantitative and objective performance evaluation can better assist the coach in this regard. Inertial measurement units (IMUs) are used in swimming for objective performance evaluation. In this study, we propose a new performance evaluation feedback (SmartSwim) using IMU and investigate its effects on the swimmer's weekly progress. Measurements were conducted each week with 15 competitive swimmers for 10 weeks using a Sacrum IMU. The SmartSwim report included a comprehensive representation of performance based on goal metrics of each phase extracted from the IMU signals. The swimmers were divided into two groups: the experimental and control groups. The SmartSwim report for each swimmer in the experimental group was given to the coach, who used it to adjust the training accordingly. The results showed that the experimental group outperformed the control group when comparing each swimmer, each session and the whole sessions. At the level of each individual, more members of the experimental group showed significant downward trend of average lap time (Mann-Kendall trend test, 95% confidence level). While comparing the sessions, the experimental group showed significantly lower lap time than the control group from the sixth session onwards (p-value < 0.05 from t-test). Considering all sessions, the experimental group showed significantly higher progress, lower average lap time, and more consistent records (Mann-Whitney U test at 95% confidence level) than the control group. This study demonstrated that SmartSwim can assist coaching by quantitatively assessing swimmers' performance, leading to more efficient training.

Putting Temperature into the Equation: Development and Validation of Algorithms to Distinguish Non-Wearing from Inactivity and Sleep in Wearable Sensors.

Long-term monitoring of real-life physical activity (PA) using wearable devices is increasingly used in clinical and epidemiological studies. The quality of the recorded data is an important issue, as unreliable data may negatively affect the outcome measures. A potential source of bias in PA assessment is the non-wearing of a device during the expected monitoring period. Identification of non-wear time is usually performed as a pre-processing step using data recorded by the accelerometer, which is the most common sensor used for PA analysis algorithms. The main issue is the correct differentiation between non-wear time, sleep time, and sedentary wake time, especially in frail older adults or patient groups. Based on the current state of the art, the objectives of this study were to (1) develop robust non-wearing detection algorithms based on data recorded with a wearable device that integrates acceleration and temperature sensors; (2) validate the algorithms using real-world data recorded according to an appropriate measurement protocol. A comparative evaluation of the implemented algorithms indicated better performances (99%, 97%, 99%, and 98% for sensitivity, specificity, accuracy, and negative predictive value, respectively) for an event-based detection algorithm, where the temperature sensor signal was appropriately processed to identify the timing of device removal/non-wear.

Robustness of In-Laboratory and Daily-Life Gait Speed Measures over One Year in High Functioning 61- to 70-Year-Old Adults.

INTRODUCTION: Gait speed is a simple and safe measure with strong predictive value for negative health outcomes in clinical practice, yet in-laboratory gait speed seems not representative for daily-life gait speed. This study aimed to investigate the interrelation between and robustness of in-laboratory and daily-life gait speed measures over 12 months in 61- to 70-year-old adults. METHODS: Gait speed was assessed in laboratory through standardized stopwatch tests and in daily life by 7 days of trunk accelerometry in the PreventIT cohort, at baseline, and after 6 and 12 months. The interrelation was investigated using Pearson's correlations between gait speed measures at each time point. For robustness, changes over time and variance components were assessed by ANOVA and measurement agreement over time by Bland-Altman analyses. RESULTS: Included were 189 participants (median age 67 years [interquartile range: 64-68], 52.2% females). In-laboratory and daily-life gait speed measures showed low correlations (Pearson's r = 0.045-0.455) at each time point. Moreover, both in-laboratory and daily-life gait speed measures appeared robust over time, with comparable and smaller within-subject than between-subject variance (range 0.001-0.095 m/s and 0.032-0.397 m/s, respectively) and minimal differences between measurements over time (Bland-Altman) with wide limits of agreement (standard deviation of mean difference range: 0.12-0.34 m/s). DISCUSSION/CONCLUSION: In-laboratory and daily-life gait speed measures show robust assessments of gait speed over 12 months and are distinct constructs in this population of high-functioning adults. This suggests that (a combination of) both measures may have added value in predicting health outcomes.

Falls Efficacy Is Associated With Better Gait and Functional Outcomes After Rehabilitation in Older Patients.

OBJECTIVE: To examine the relationship between falls efficacy and the change in gait speed and functional status in older patients undergoing postacute rehabilitation. DESIGN: Prospective cohort study. SETTING: Postacute rehabilitation facility. PARTICIPANTS: Patients (N=180) aged 65 years and older (mean age ± SD, 81.3±7.1y). INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Data on demographics; functional, cognitive, and affective status; and falls efficacy using a 10-item version of the Falls Efficacy Scale (FES; range, 0-100) were collected upon admission. Data about gait speed and functional status (Barthel Index and Basic Activities of Daily Living [BADL]) were measured at admission and discharge. In addition, BADL performance was self-reported 1 month after discharge. RESULTS: Compared with admission, all rehabilitation outcomes improved at discharge: gait speed (0.41±0.15 m/s vs 0.50±0.16 m/s; P<.001), Barthel Index score (68.4±16.3 vs 82.5±13.6; P<.001), and BADL (3.5±1.6 vs 4.7±1.3; P<.001). Adjusting for baseline status and other potential confounders, baseline FES independently predicted gait speed (adjusted coefficient: 0.002; 95% confidence interval [CI], 0.000-0.004; P=.025) and Barthel index (adjusted coefficient: 0.225; 95% CI, 0.014-0.435; P=.037) at discharge, with higher confidence at baseline predicting greater improvement. Baseline FES was also independently associated with self-reported BADL performance at the 1-month follow-up (adjusted coefficient: 0.020; 95% CI, 0.010-0.031; P<.001). CONCLUSIONS: In older patients, higher falls efficacy predicted better gait and functional rehabilitation outcomes, independently of baseline performance. These results suggest that interventions aiming at falls efficacy improvement during rehabilitation might also contribute to enhancing gait speed and functional status in patients admitted to this setting.

Continuous Analysis of Marathon Running Using Inertial Sensors: Hitting Two Walls?

Marathon running involves complex mechanisms that cannot be measured with objective metrics or laboratory equipment. The emergence of wearable sensors introduced new opportunities, allowing the continuous recording of relevant parameters. The present study aimed to assess the evolution of stride-by-stride spatio-temporal parameters, stiffness, and foot strike angle during a marathon and determine possible abrupt changes in running patterns. Twelve recreational runners were equipped with a Global Navigation Satellite System watch, and two inertial measurement units clamped on each foot during a marathon race. Data were split into eight 5-km sections and only level parts were analyzed. We observed gradual increases in contact time and duty factor as well as decreases in flight time, swing time, stride length, speed, maximal vertical force and stiffness during the race. Surprisingly, the average foot strike angle decreased during the race, but each participant maintained a rearfoot strike until the end. Two abrupt changes were also detected around km 25 and km 35. These two breaks are possibly due to the alteration of the stretch-shortening cycle combined with physiological limits. This study highlights new measurable phenomena that can only be analyzed through continuous monitoring of runners over a long period of time.

Indirect Estimation of Breathing Rate from Heart Rate Monitoring System during Running.

Recent advances in wearable technologies integrating multi-modal sensors have enabled the in-field monitoring of several physiological metrics. In sport applications, wearable devices have been widely used to improve performance while minimizing the risk of injuries and illness. The objective of this project is to estimate breathing rate (BR) from respiratory sinus arrhythmia (RSA) using heart rate (HR) recorded with a chest belt during physical activities, yielding additional physiological insight without the need of an additional sensor. Thirty-one healthy adults performed a run at increasing speed until exhaustion on an instrumented treadmill. RR intervals were measured using the Polar H10 HR monitoring system attached to a chest belt. A metabolic measurement system was used as a reference to evaluate the accuracy of the BR estimation. The evaluation of the algorithms consisted of exploring two pre-processing methods (band-pass filters and relative RR intervals transformation) with different instantaneous frequency tracking algorithms (short-term Fourier transform, single frequency tracking, harmonic frequency tracking and peak detection). The two most accurate BR estimations were achieved by combining band-pass filters with short-term Fourier transform, and relative RR intervals transformation with harmonic frequency tracking, showing 5.5% and 7.6% errors, respectively. These two methods were found to provide reasonably accurate BR estimation over a wide range of breathing frequency. Future challenges consist in applying/validating our approaches during in-field endurance running in the context of fatigue assessment.

Comparison of Laboratory and Daily-Life Gait Speed Assessment during ON and OFF States in Parkinson's Disease.

Accurate assessment of Parkinson's disease (PD) ON and OFF states in the usual environment is essential for tailoring optimal treatments. Wearables facilitate measurements of gait in novel and unsupervised environments; however, differences between unsupervised and in-laboratory measures have been reported in PD. We aimed to investigate whether unsupervised gait speed discriminates medication states and which supervised tests most accurately represent home performance. In-lab gait speeds from different gait tasks were compared to home speeds of 27 PD patients at ON and OFF states using inertial sensors. Daily gait speed distribution was expressed in percentiles and walking bout (WB) length. Gait speeds differentiated ON and OFF states in the lab and the home. When comparing lab with home performance, ON assessments in the lab showed moderate-to-high correlations with faster gait speeds in unsupervised environment (r = 0.69; p < 0.001), associated with long WB. OFF gait assessments in the lab showed moderate correlation values with slow gait speeds during OFF state at home (r = 0.56; p = 0.004), associated with short WB. In-lab and daily assessments of gait speed with wearables capture additional integrative aspects of PD, reflecting different aspects of mobility. Unsupervised assessment using wearables adds complementary information to the clinical assessment of motor fluctuations in PD.

A Robotic Glenohumeral Simulator for Investigating Prosthetic Implant Subluxation.

Total shoulder arthroplasty (TSA) is an effective treatment for glenohumeral (GH) osteoarthritis. However, it still suffers from a substantial rate of mechanical failure, which may be related to cyclic off-center loading of the humeral head on the glenoid. In this work, we present the design and evaluation of a GH joint robotic simulator developed to study GH translations. This five-degree-of-freedom robot was designed to replicate the rotations (±40 deg, accuracy 0.5 deg) and three-dimensional (3D) forces (up to 2 kN, with a 1% error settling time of 0.6 s) that the humeral implant exerts on the glenoid implant. We tested the performances of the simulator using force patterns measured in real patients. Moreover, we evaluated the effect of different orientations of the glenoid implant on joint stability. When simulating realistic dynamic forces and implant orientations, the simulator was able to reproduce stable behavior by measuring the translations of the humeral head of less than 24 mm with respect to the glenoid implant. Simulation with quasi-static forces showed dislocation in extreme ranges of implant orientation. The robotic GH simulator presented here was able to reproduce physiological GH forces and may therefore be used to further evaluate the effects of glenoid implant design and orientation on joint stability.

Postural transitions detection and characterization in healthy and patient populations using a single waist sensor.

BACKGROUND: Sit-to-stand and stand-to-sit transitions are frequent daily functional tasks indicative of muscle power and balance performance. Monitoring these postural transitions with inertial sensors provides an objective tool to assess mobility in both the laboratory and home environment. While the measurement depends on the sensor location, the clinical and everyday use requires high compliance and subject adherence. The objective of this study was to propose a sit-to-stand and stand-to-sit transition detection algorithm that works independently of the sensor location. METHODS: For a location-independent algorithm, the vertical acceleration of the lower back in the global frame was used to detect the postural transitions in daily activities. The detection performance of the algorithm was validated against video observations. To investigate the effect of the location on the kinematic parameters, these parameters were extracted during a five-time sit-to-stand test and were compared for different locations of the sensor on the trunk and lower back. RESULTS: The proposed detection method demonstrates high accuracy in different populations with a mean positive predictive value (and mean sensitivity) of 98% (95%) for healthy individuals and 89% (89%) for participants with diseases. CONCLUSIONS: The sensor location around the waist did not affect the performance of the algorithm in detecting the sit-to-stand and stand-to-sit transitions. However, regarding the accuracy of the kinematic parameters, the sensors located on the sternum and L5 vertebrae demonstrated the highest reliability.

Reply to Comments: Hurdle Clearance Detection and Spatiotemporal Analysis in 400 Meters Hurdles Races Using Shoe-Mounted Magnetic and Inertial Sensor.

The current document answers the comment addressed by Schmidt, M [...].

Hurdle Clearance Detection and Spatiotemporal Analysis in 400 Meters Hurdles Races Using Shoe-Mounted Magnetic and Inertial Sensors.

This research aimed to determine whether: (1) shoe-worn magnetic and inertial sensors can be used to detect hurdle clearance and identify the leading leg in 400-m hurdles, and (2) to provide an analysis of the hurdlers' spatiotemporal parameters in the intervals defined by the hurdles' position. The data set is composed of MIMU recordings of 15 athletes in a competitive environment. The results show that the method based on the duration of the flight phase was able to detect hurdle clearance and identify the leading leg with 100% accuracy. Moreover, by combining the swing phase duration with the orientation of the foot, we achieved, in unipedal configuration, 100% accuracy in hurdle clearance detection, and 99.7% accuracy in the identification of the leading leg. Finally, this study provides statistical evidence that contact time significantly increases, while speed and step frequency significantly decrease with time during 400 m hurdle races.

Correction to: Locomotion and cadence detection using a single trunk-fixed accelerometer: validity for children with cerebral palsy in daily life-like conditions.

The original article [1] contained a minor error whereby the middle initial of Christopher J. Newman's name was mistakenly omitted.

Locomotion and cadence detection using a single trunk-fixed accelerometer: validity for children with cerebral palsy in daily life-like conditions.

BACKGROUND: Physical therapy interventions for ambulatory youth with cerebral palsy (CP) often focus on activity-based strategies to promote functional mobility and participation in physical activity. The use of activity monitors validated for this population could help to design effective personalized interventions by providing reliable outcome measures. The objective of this study was to devise a single-sensor based algorithm for locomotion and cadence detection, robust to atypical gait patterns of children with CP in the real-life like monitoring conditions. METHODS: Study included 15 children with CP, classified according to Gross Motor Function Classification System (GMFCS) between levels I and III, and 11 age-matched typically developing (TD). Six IMU devices were fixed on participant's trunk (chest and low back/L5), thighs, and shanks. IMUs on trunk were independently used for development of algorithm, whereas the ensemble of devices on lower limbs were used as reference system. Data was collected according to a semi-structured protocol, and included typical daily-life activities performed indoor and outdoor. The algorithm was based on detection of peaks associated to heel-strike events, identified from the norm of trunk acceleration signals, and included several processing stages such as peak enhancement and selection of the steps-related peaks using heuristic decision rules. Cadence was estimated using time- and frequency-domain approaches. Performance metrics were sensitivity, specificity, precision, error, intra-class correlation coefficient, and Bland-Altman analysis. RESULTS: According to GMFCS, CP children were classified as GMFCS I (n = 7), GMFCS II (n = 3) and GMFCS III (n = 5). Mean values of sensitivity, specificity and precision for locomotion detection ranged between 0.93-0.98, 0.92-0.97 and 0.86-0.98 for TD, CP-GMFCS I and CP-GMFCS II-III groups, respectively. Mean values of absolute error for cadence estimation (steps/min) were similar for both methods, and ranged between 0.51-0.88, 1.18-1.33 and 1.94-2.3 for TD, CP-GMFCS I and CP-GMFCS II-III groups, respectively. The standard deviation was higher in CP-GMFCS II-III group, the lower performances being explained by the high variability of atypical gait patterns. CONCLUSIONS: The algorithm demonstrated good performance when applied to a wide range of gait patterns, from normal to the pathological gait of highly affected children with CP using walking aids.