Gait Pattern Analysis: Integration of a Highly Sensitive Flexible Pressure Sensor on a Wireless Instrumented Insole.

Gait phase monitoring wearable sensors play a crucial role in assessing both health and athletic performance, offering valuable insights into an individual's gait pattern. In this study, we introduced a simple and cost-effective capacitive gait sensor manufacturing approach, utilizing a micropatterned polydimethylsiloxane dielectric layer placed between screen-printed silver electrodes. The sensor demonstrated inherent stretchability and durability, even when the electrode was bent at a 45-degree angle, it maintained an electrode resistance of approximately 3 Ω. This feature is particularly advantageous for gait monitoring applications. Furthermore, the fabricated flexible capacitive pressure sensor exhibited higher sensitivity and linearity at both low and high pressure and displayed very good stability. Notably, the sensors demonstrated rapid response and recovery times for both under low and high pressure. To further explore the capabilities of these new sensors, they were successfully tested as insole-type pressure sensors for real-time gait signal monitoring. The sensors displayed a well-balanced combination of sensitivity and response time, making them well-suited for gait analysis. Beyond gait analysis, the proposed sensor holds the potential for a wide range of applications within biomedical, sports, and commercial systems where soft and conformable sensors are preferred.

Ambient floor vibration sensing advances the accessibility of functional gait assessments for children with muscular dystrophies.

Muscular dystrophies (MD) are a group of genetic neuromuscular disorders that cause progressive weakness and loss of muscles over time, influencing 1 in 3500-5000 children worldwide. New and exciting treatment options have led to a critical need for a clinical post-marketing surveillance tool to confirm the efficacy and safety of these treatments after individuals receive them in a commercial setting. For MDs, functional gait assessment is a common approach to evaluate the efficacy of the treatments because muscle weakness is reflected in individuals' walking patterns. However, there is little incentive for the family to continue to travel for such assessments due to the lack of access to specialty centers. While various existing sensing devices, such as cameras, force plates, and wearables can assess gait at home, they are limited by privacy concerns, area of coverage, and discomfort in carrying devices, which is not practical for long-term, continuous monitoring in daily settings. In this study, we introduce a novel functional gait assessment system using ambient floor vibrations, which is non-invasive and scalable, requiring only low-cost and sparsely deployed geophone sensors attached to the floor surface, suitable for in-home usage. Our system captures floor vibrations generated by footsteps from patients while they walk around and analyzes such vibrations to extract essential gait health information. To enhance interpretability and reliability under various sensing scenarios, we translate the signal patterns of floor vibration to pathological gait patterns related to MD, and develop a hierarchical learning algorithm that aggregates insights from individual footsteps to estimate a person's overall gait performance. When evaluated through real-world experiments with 36 subjects (including 15 patients with MD), our floor vibration sensing system achieves a 94.8% accuracy in predicting functional gait stages for patients with MD. Our approach enables accurate, accessible, and scalable functional gait assessment, bringing MD progressive tracking into real life.

The Analytical Validity of Stride Detection and Gait Parameters Reconstruction Using the Ankle-Mounted Inertial Measurement Unit Syde®.

The increasing use of inertial measurement units (IMU) in biomedical sciences brings new possibilities for clinical research. The aim of this paper is to demonstrate the accuracy of the IMU-based wearable Syde® device, which allows day-long and remote continuous gait recording in comparison to a reference motion capture system. Twelve healthy subjects (age: 23.17 ± 2.04, height: 174.17 ± 6.46 cm) participated in a controlled environment data collection and performed a series of gait tasks with both systems attached to each ankle. A total of 2820 strides were analyzed. The results show a median absolute stride length error of 1.86 cm between the IMU-based wearable device reconstruction and the motion capture ground truth, with the 75th percentile at 3.24 cm. The median absolute stride horizontal velocity error was 1.56 cm/s, with the 75th percentile at 2.63 cm/s. With a measurement error to the reference system of less than 3 cm, we conclude that there is a valid physical recovery of stride length and horizontal velocity from data collected with the IMU-based wearable Syde® device.

Wearable Triboelectric Sensors Enabled Gait Analysis and Waist Motion Capture for IoT-Based Smart Healthcare Applications.

Gait and waist motions always contain massive personnel information and it is feasible to extract these data via wearable electronics for identification and healthcare based on the Internet of Things (IoT). There also remains a demand to develop a cost-effective human-machine interface to enhance the immersion during the long-term rehabilitation. Meanwhile, triboelectric nanogenerator (TENG) revealing its merits in both wearable electronics and IoT tends to be a possible solution. Herein, the authors present wearable TENG-based devices for gait analysis and waist motion capture to enhance the intelligence and performance of the lower-limb and waist rehabilitation. Four triboelectric sensors are equidistantly sewed onto a fabric belt to recognize the waist motion, enabling the real-time robotic manipulation and virtual game for immersion-enhanced waist training. The insole equipped with two TENG sensors is designed for walking status detection and a 98.4% identification accuracy for five different humans aiming at rehabilitation plan selection is achieved by leveraging machine learning technology to further analyze the signals. Through a lower-limb rehabilitation robot, the authors demonstrate that the sensory system performs well in user recognition, motion monitoring, as well as robot and gaming-aided training, showing its potential in IoT-based smart healthcare applications.

Prediction of fall risk among community-dwelling older adults using a wearable system.

Falls are among the most common cause of decreased mobility and independence in older adults and rank as one of the most severe public health problems with frequent fatal consequences. In the present study, gait characteristics from 171 community-dwelling older adults were evaluated to determine their predictive ability for future falls using a wearable system. Participants wore a wearable sensor (inertial measurement unit, IMU) affixed to the sternum and performed a 10-m walking test. Measures of gait variability, complexity, and smoothness were extracted from each participant, and prospective fall incidence was evaluated over the following 6-months. Gait parameters were refined to better represent features for a random forest classifier for the fall-risk classification utilizing three experiments. The results show that the best-trained model for faller classification used both linear and nonlinear gait parameters and achieved an overall 81.6 ± 0.7% accuracy, 86.7 ± 0.5% sensitivity, 80.3 ± 0.2% specificity in the blind test. These findings augment the wearable sensor's potential as an ambulatory fall risk identification tool in community-dwelling settings. Furthermore, they highlight the importance of gait features that rely less on event detection methods, and more on time series analysis techniques. Fall prevention is a critical component in older individuals' healthcare, and simple models based on gait-related tasks and a wearable IMU sensor can determine the risk of future falls.

Proposed objective scoring algorithm for walking performance, based on relevant gait metrics: the Simplified Mobility Score (SMoS™)-observational study.

BACKGROUND: Walking is a fundamental part of living, and its importance is not limited by age or medical status. Reduced walking speed (WS), or gait velocity, is a sign of advancing age, various disease states, cognitive impairment, mental illness and early mortality. Activity levels, as defined in the literature as "daily step count" (DSC), is also a relevant measure of health status. A deterioration in our walking metrics, such as reduced WS and DSC, is associated with poor health outcomes. These objective measures are of such importance, that walking speed has been dubbed "the 6th vital sign". We report a new objective measure that scores walking using the relevant metrics of walking speed and daily step count, into an easy-to-understand score from 0 (nil mobility) to 100 (excellent mobility), termed the Simplified Mobility Score (SMoS™). We have provided equal weighting to walking speed and daily step count, using a simple algorithm to score each metric out of 50. METHODS: Gait data was collected from 182 patients presenting to a tertiary hospital spinal unit with complaints of pain and reduced mobility. Walking speed was measured from a timed walk along an unobstructed pathway. Daily step count information was obtained from patients who had enabled step count tracking on their devices. The SMoS of the sample group were compared to expected population values calculated from the literature using 2-tailed Z tests. RESULTS: There were significantly reduced SMoS in patients who presented to the spinal unit than those expected at each age group for both genders, except for the 50-59 age bracket where no statistically significant reduction was observed. Even lower scores were present in those that went on to have surgical management. There was a significant correlation of SMoS scores with subjective disability scores such as the Oswestry Disability Index (ODI) and Visual Analogue Scale (VAS) in this cohort. CONCLUSIONS: The SMoS is a simple and effective scoring tool which is demonstrably altered in spinal patients across age and gender brackets and correlates well with subjective disability scores. The SMoS has the potential to be used as a screening tool in primary and specialised care settings.

Validation of a portable marker-based motion analysis system.

BACKGROUND: The Opti_Knee system, a marker-based motion capture system, tracks and analyzes the 6 degrees of freedom (6DOF) motion of the knee joint. However, the validation of the accuracy of this gait system had not been previously reported. The objective of this study was to validate and the system. Two healthy subjects were recruited for the study. METHODS: The 6DOF kinematics of the knee during flexion-extension and level walking cycles of the knee were recorded by Opti_Knee and compared to those from a biplanar fluoroscopy system. The root mean square error (RMSE) of knee kinematics in flexion-extension cycles were compared between the two systems to validate the accuracy at which they detect basic knee motions. The RMSE of kinematics at key events of gait cycles (level walking) were compared to validate the accuracy at which the systems detect functional knee motion. Pearson correlation tests were conducted to assess similarities in knee kinematic trends between the two systems. RESULTS: In flexion-extension cycles, the average translational accuracy (RMSE) was between 2.7 and 3.7 mm and the average rotational accuracy was between 1.7 and 3.8°. The Pearson correlation of coefficients for flexion-extension cycles was between 0.858 and 0.994 for translation and 0.995-0.999 for angles. In gait cycles, the RMSEs of angular knee kinematics were 2.3° for adduction/abduction, 3.2° for internal/external rotation, and 1.4° for flexion/extension. The RMSEs of translational kinematics were 4.2 mm for anterior/posterior translation, 3.3 mm for distal/proximal translation, and 3.2 mm for medial/lateral translation. The Pearson correlation of coefficients values was between 0.964 and 0.999 for angular kinematics and 0.883 and 0.938 for translational kinematics. CONCLUSION: The Opti_Knee gait system exhibited acceptable accuracy and strong correlation strength compared to biplanar fluoroscopy. The Opti _Knee may serve as a promising portable clinical system for dynamic functional assessments of the knee.

Validation of IMU-based gait event detection during curved walking and turning in older adults and Parkinson's Disease patients.

BACKGROUND: Identification of individual gait events is essential for clinical gait analysis, because it can be used for diagnostic purposes or tracking disease progression in neurological diseases such as Parkinson's disease. Previous research has shown that gait events can be detected from a shank-mounted inertial measurement unit (IMU), however detection performance was often evaluated only from straight-line walking. For use in daily life, the detection performance needs to be evaluated in curved walking and turning as well as in single-task and dual-task conditions. METHODS: Participants (older adults, people with Parkinson's disease, or people who had suffered from a stroke) performed three different walking trials: (1) straight-line walking, (2) slalom walking, (3) Stroop-and-walk trial. An optical motion capture system was used a reference system. Markers were attached to the heel and toe regions of the shoe, and participants wore IMUs on the lateral sides of both shanks. The angular velocity of the shank IMUs was used to detect instances of initial foot contact (IC) and final foot contact (FC), which were compared to reference values obtained from the marker trajectories. RESULTS: The detection method showed high recall, precision and F1 scores in different populations for both initial contacts and final contacts during straight-line walking (IC: recall [Formula: see text] 100%, precision [Formula: see text] 100%, F1 score [Formula: see text] 100%; FC: recall [Formula: see text] 100%, precision [Formula: see text] 100%, F1 score [Formula: see text] 100%), slalom walking (IC: recall [Formula: see text] 100%, precision [Formula: see text] 99%, F1 score [Formula: see text]100%; FC: recall [Formula: see text] 100%, precision [Formula: see text] 99%, F1 score [Formula: see text]100%), and turning (IC: recall [Formula: see text] 85%, precision [Formula: see text] 95%, F1 score [Formula: see text]91%; FC: recall [Formula: see text] 84%, precision [Formula: see text] 95%, F1 score [Formula: see text]89%). CONCLUSIONS: Shank-mounted IMUs can be used to detect gait events during straight-line walking, slalom walking and turning. However, more false events were observed during turning and more events were missed during turning. For use in daily life we recommend identifying turning before extracting temporal gait parameters from identified gait events.

Foot progression angle estimation using a single foot-worn inertial sensor.

BACKGROUND: The foot progression angle is an important measure used to help patients reduce their knee adduction moment. Current measurement systems are either lab-bounded or do not function in all environments (e.g., magnetically distorted). This work proposes a novel approach to estimate foot progression angle using a single foot-worn inertial sensor (accelerometer and gyroscope). METHODS: The approach uses a dynamic step frame that is recalculated for the stance phase of each step to calculate the foot trajectory relative to that frame, to minimize effects of drift and to eliminate the need for a magnetometer. The foot progression angle (FPA) is then calculated as the angle between walking direction and the dynamic step frame. This approach was validated by gait measurements with five subjects walking with three gait types (normal, toe-in and toe-out). RESULTS: The FPA was estimated with a maximum mean error of ~ 2.6° over all gait conditions. Additionally, the proposed inertial approach can significantly differentiate between the three different gait types. CONCLUSION: The proposed approach can effectively estimate differences in FPA without requiring a heading reference (magnetometer). This work enables feedback applications on FPA for patients with gait disorders that function in any environment, i.e. outside of a gait lab or in magnetically distorted environments.

Measuring freezing of gait during daily-life: an open-source, wearable sensors approach.

BACKGROUND: Although a growing number of studies focus on the measurement and detection of freezing of gait (FoG) in laboratory settings, only a few studies have attempted to measure FoG during daily life with body-worn sensors. Here, we presented a novel algorithm to detect FoG in a group of people with Parkinson's disease (PD) in the laboratory (Study I) and extended the algorithm in a second cohort of people with PD at home during daily life (Study II). METHODS: In Study I, we described of our novel FoG detection algorithm based on five inertial sensors attached to the feet, shins and lumbar region while walking in 40 participants with PD. We compared the performance of the algorithm with two expert clinical raters who scored the number of FoG episodes from video recordings of walking and turning based on duration of the episodes: very short (< 1 s), short (2-5 s), and long (> 5 s). In Study II, a different cohort of 48 people with PD (with and without FoG) wore 3 wearable sensors on their feet and lumbar region for 7 days. Our primary outcome measures for freezing were the % time spent freezing and its variability. RESULTS: We showed moderate to good agreement in the number of FoG episodes detected in the laboratory (Study I) between clinical raters and the algorithm (if wearable sensors were placed on the feet) for short and long FoG episodes, but not for very short FoG episodes. When extending this methodology to unsupervised home monitoring (Study II), we found that percent time spent freezing and the variability of time spent freezing differentiated between people with and without FoG (p < 0.05), and that short FoG episodes account for 69% of the total FoG episodes. CONCLUSION: Our findings showed that objective measures of freezing in PD using inertial sensors on the feet in the laboratory are matching well with clinical scores. Although results found during daily life are promising, they need to be validated. Objective measures of FoG with wearable technology during community-living would be useful for managing this distressing feature of mobility disability in PD.

Agreement and consistency of five different clinical gait analysis systems in the assessment of spatiotemporal gait parameters.

BACKGROUND: Measuring gait function has become an essential tool in the assessment of mobility in aging populations for both, clinicians and researchers. A variety of systems exist that assess gait parameters such as gait cycle time, gait speed or duration of relative gait phases. Due to different measurement principles such as inertial or pressure sensors, accurate detection of spatiotemporal events may vary between systems. RESEARCH QUESTION: To compare the absolute agreement and consistency in spatiotemporal gait parameters among five different clinical gait analysis systems using different sensor technologies. METHODS: We compared two devices using inertial sensors (GaitUp & Mobility Lab), two devices using pressure sensor systems (GAITRite & Zebris) as well as one optical system (OptoGait). Twelve older adults walked at self-selected speed through a walkway integrating all of the above systems. Basic spatiotemporal parameters (gait cycle time, cadence, gait speed and stride length) as well as measures of relative phase (stance phase, swing phase, double stance phase, single limb support) were extracted from all systems. We used Intraclass Correlation Coefficients as measures of agreement and consistency. RESULTS: High agreement and consistency between all systems was found for basic spatiotemporal parameters, whereas parameters of relative phase showed poorer agreement and consistency. Overground measurement (GAITRite & OptoGait) showed generally higher agreement with each other as compared to inertial sensor-based systems. SIGNIFICANCE: Our results indicate that accurate detection of both, the heel-strike and toe-off event are crucial for reliable results. Systematic errors in the detection of one or both events may only have a small impact on basic spatiotemporal outcomes as errors remain consistent from step to step. Relative phase parameters on the other hand may be affected to a much larger extent as these differences lead to a systematic increase or reduction of relative phase durations.

Reliability of inertial sensor based spatiotemporal gait parameters for short walking bouts in community dwelling older adults.

BACKGROUND: When performing quantitative analysis of gait in older adults we need to strike a balance between capturing sufficient data for reliable measurement and avoiding issues such as fatigue. The optimal bout duration is that which contains sufficient gait cycles to enable a reliable and representative estimate of gait performance. RESEARCH QUESTION: How does the number of gait cycles in a walking bout influence reliability of spatiotemporal gait parameters measured using body-worn inertial sensors in a cohort of community dwelling older adults? METHODS: One hundred and fifteen (115) community dwelling older adults executed three 30-metre walk trials in a single measurement session. Bilateral gait data were collected using two inertial sensors attached to each participant's right and left shank, and gait events detected from the medio-lateral angular velocity signal. The number of gait cycles selected from each walking trial was varied from 3 to 16. Intraclass correlation coefficients (ICC(2,k)) were calculated to evaluate the reliability of each spatiotemporal gait parameter according to the number of gait cycles included in the analysis. RESULTS: The specified algorithm and the clipping procedure for extracting short bouts of gait data seem appropriate for assessing older adults, providing reliable spatiotemporal measures from three gait cycles (three strides per leg) and good reliability for most parameters describing gait variability and gait asymmetry after six gait cycles (six strides per leg). SIGNIFICANCE: A combination of using bilateral sensor data and adaptive thresholds for gait event detection enable reliable measures of spatiotemporal gait parameters over short walking bouts (minimum six gait cycles) in community dwelling older adults. This opens new possibilities in the use of wearable sensors in gait assessment based on short walking tasks. We recommend the number of gait cycles should be reported along with the calculated measures as reference values.

Unicompartmental knee arthroplasty results in a better gait pattern than total knee arthroplasty: Gait analysis with a smartphone application.

OBJECTIVES: The aim of this study was to compare the smartphone- based gait analysis data of patients who underwent total knee arthroplasty (TKA) and unicompartmental knee arthroplasty (UKA). PATIENTS AND METHODS: Between January 2016 and April 2019, a total of 51 patients (3 males, 48 females; mean age: 60.92 years; range, 51 to 70 years) who were operated with UKA or TKA in our clinic were retrospectively analyzed. The patients were divided into two groups according to the type of procedure as the UKA group (n=17) and unilateral TKA group (n=34). Gait analysis was made via a smartphone application (Gait Analyzer software version 0.9.95.0) with data acquired from the accelerometer of the smartphone. This analysis was performed using data collected from the Acceleration Sensor LSM6DSO into the Samsung Galaxy Note 10 Plus phone. Gait velocity, step time, step length, cadence, step time symmetry, step length symmetry, and vertical COM (vert-COM) parameters were measured. RESULTS: There were no statistically significant differences between the groups in respect of age, sex, body mass index, operated side, and follow-up duration. Compared to the TKA group, the UKA patients showed a better gait pattern in gait velocity (p=0.03), step time symmetry (p=0.005), and step length symmetry (p=0.024). No significant difference was detected in step time (p=0.807), step length (p=0.302), cadence (p=0.727) and vert-COM parameters (p=0.608). CONCLUSION: The gait of UKA patients is closer to the physiological pattern with a better gait velocity, step time symmetry, and step length symmetry than TKA patients. The surgical treatment option of UKA for knee medial compartment osteoarthritis leads to a better gait pattern than TKA.

Reliability testing of the heel marker in three-dimensional gait analysis.

INTRODUCTION: In three-dimensional gait analysis, anatomical axes are defined by and therefore sensitive to marker placement. Previous analysis of the Oxford Foot Model (OFM) has suggested that the axes of the hindfoot are most sensitive to marker placement on the posterior aspect of the heel. Since other multi-segment foot models also use a similar marker, it is important to find methods to place this as accurately as possible. The aim of this pilot study was to test two different 'jigs' (anatomical alignment devices) against eyeball marker placement to improve reliability of heel marker placement and calculation of hindfoot angles using the OFM. METHODS: Two jigs were designed using three-dimensional printing: a ratio caliper and heel mould. OFM kinematics were collected for ten healthy adults; intra-tester and inter-tester repeatability of hindfoot marker placement were assessed using both an experienced and inexperienced gait analyst for 5 clinically relevant variables. RESULTS: For 3 out of 5 variables the intra-tester and inter-tester variability was below 2 degrees for all methods of marker placement. The ratio caliper had the lowest intra-tester variability for the experienced gait analyst in all 5 variables and for the inexperienced gait analyst in 4 out of 5 variables. However for inter-tester variability, the ratio caliper was only lower than the eyeball method in 2 out of the 5 variables. The mould produced the worst results for 3 of the 5 variables, and was particularly prone to variability when assessing average hindfoot rotation, making it the least reliable method overall. CONCLUSIONS: The use of the ratio caliper may improve intra-tester variability, but does not seem superior to the eyeball method of marker placement for inter-tester variability. The use of a heel mould is discouraged.

FeetMe® Monitor-connected insoles are a valid and reliable alternative for the evaluation of gait speed after stroke.

BACKGROUND: One of the main challenges after stroke is gait recovery. To provide patients with an individualized rehabilitation program, it is helpful to have real-life objective evaluations at baseline and at regular follow-ups to adjust the program and verify potential improvements. OBJECTIVES: To evaluate the accuracy and reliability of a fully stand-alone system of connected insoles (FeetMe® Monitor) against a widely used clinical walkway system (GAITRite®). METHODS: Twenty-nine subjects with a stroke that occurred >6 months prior participated in the study. Their comfortable gait over three 8-m trials was evaluated by four raters, on Day 1 and Day 7, using simultaneously FeetMe® Monitor and GAITRite®. Velocity, stride length, cadence, stance, and swing duration were calculated on both sides over three sequences of gait: one single stride, 8 m, and three 8-m trials pooled. The Intra-class Correlation Coefficient (ICC) and the Bland-Altman plot evaluated the construct validity (inter-device) and the reliability (test-retest and inter-rater) of FeetMe® Monitor. RESULTS: Through all gait analysis sequences, the inter-device ICCs were >0.95 for velocity, stride length, and cadence. Ranges of inter-device ICCs were [0.77-0.94] for stance duration for both limbs, and for swing duration [0.32-0.57] on the non-paretic side and [0.75-0.90] on the paretic side. Test-retest and inter-rater ICCs for all parameters were >0.73 for one single stride, >0.88 for 8-m trials and >0.94 for three 8-m trials. CONCLUSION: FeetMe® Monitor is an accurate and reliable system for measurement of gait velocity, stride length, cadence, and stance duration in chronic hemiparesis.

Biomechanical effects following footstrike pattern modification using wearable sensors.

OBJECTIVES: This study sought to examine the biomechanical effects of an in-field sensor-based gait retraining program targeting footstrike pattern modification during level running, uphill running and downhill running. DESIGN: Quasi-experimental design. METHODS: Sixteen habitual rearfoot strikers were recruited. All participants underwent a baseline evaluation on an instrumented treadmill at their preferred running speeds on three slope settings. Participants were then instructed to modify their footstrike pattern from rearfoot to non-rearfoot strike with real-time audio biofeedback in an 8-session in-field gait retraining program. A reassessment was conducted to evaluate the post-training biomechanical effects. Footstrike pattern, footstrike angle, vertical instantaneous loading rate (VILR), stride length, cadence, and knee flexion angle at initial contact were measured and compared. RESULTS: No significant interaction was found between training and slope conditions for all tested variables. Significant main effects were observed for gait retraining (p-values≤0.02) and slopes (p-values≤0.01). After gait retraining, 75% of the participants modified their footstrike pattern during level running, but effects of footstrike pattern modification were inconsistent between slopes. During level running, participants exhibited a smaller footstrike angle (p≤0.01), reduced VILR (p≤0.01) and a larger knee flexion angle (p=0.01). Similar effects were found during uphill running, together with a shorter stride length (p=0.01) and an increased cadence (p≤0.01). However, during downhill running, no significant change in VILR was found (p=0.16), despite differences found in other biomechanical measurements (p-values=0.02-0.05). CONCLUSION: An 8-session in-field gait retraining program was effective in modifying footstrike pattern among runners, but discrepancies in VILR, stride length and cadence were found between slope conditions.

Inertial sensor-based gait parameters reflect patient-reported fatigue in multiple sclerosis.

BACKGROUND: Multiple sclerosis (MS) is a disabling disease affecting the central nervous system and consequently the whole body's functional systems resulting in different gait disorders. Fatigue is the most common symptom in MS with a prevalence of 80%. Previous research studied the relation between fatigue and gait impairment using stationary gait analysis systems and short gait tests (e.g. timed 25 ft walk). However, wearable inertial sensors providing gait data from longer and continuous gait bouts have not been used to assess the relation between fatigue and gait parameters in MS. Therefore, the aim of this study was to evaluate the association between fatigue and spatio-temporal gait parameters extracted from wearable foot-worn sensors and to predict the degree of fatigue. METHODS: Forty-nine patients with MS (32 women; 17 men; aged 41.6 years, EDSS 1.0-6.5) were included where each participant was equipped with a small Inertial Measurement Unit (IMU) on each foot. Spatio-temporal gait parameters were obtained from the 6-min walking test, and the Borg scale of perceived exertion was used to represent fatigue. Gait parameters were normalized by taking the difference of averaged gait parameters between the beginning and end of the test to eliminate inter-individual differences. Afterwards, normalized parameters were transformed to principle components that were used as input to a Random Forest regression model to formulate the relationship between gait parameters and fatigue. RESULTS: Six principal components were used as input to our model explaining more than 90% of variance within our dataset. Random Forest regression was used to predict fatigue. The model was validated using 10-fold cross validation and the mean absolute error was 1.38 points. Principal components consisting mainly of stride time, maximum toe clearance, heel strike angle, and stride length had large contributions (67%) to the predictions made by the Random Forest. CONCLUSIONS: The level of fatigue can be predicted based on spatio-temporal gait parameters obtained from an IMU based system. The results can help therapists to monitor fatigue before and after treatment and in rehabilitation programs to evaluate their efficacy. Furthermore, this can be used in home monitoring scenarios where therapists can monitor fatigue using IMUs reducing time and effort of patients and therapists.

A Wearable Sensor System to Measure Step-Based Gait Parameters for Parkinson's Disease Rehabilitation.

Spatiotemporal parameters of gait serve as an important biomarker to monitor gait impairments as well as to develop rehabilitation systems. In this work, we developed a computationally-efficient algorithm (SDI-Step) that uses segmented double integration to calculate step length and step time from wearable inertial measurement units (IMUs) and assessed its ability to reliably and accurately measure spatiotemporal gait parameters. Two data sets that included simultaneous measurements from wearable sensors and from a laboratory-based system were used in the assessment. The first data set utilized IMU sensors and a GAITRite mat in our laboratory to monitor gait in fifteen participants: 9 young adults (YA1) (5 females, 4 males, age 23.6 ± 1 years), and 6 people with Parkinson's disease (PD) (3 females, 3 males, age 72.3 ± 6.6 years). The second data set, which was accessed from a publicly-available repository, utilized IMU sensors and an optoelectronic system to monitor gait in five young adults (YA2) (2 females, 3 males, age 30.5 ± 3.5 years). In order to provide a complete representation of validity, we used multiple statistical analyses with overlapping metrics. Gait parameters such as step time and step length were calculated and the agreement between the two measurement systems for each gait parameter was assessed using Passing-Bablok (PB) regression analysis and calculation of the Intra-class Correlation Coefficient (ICC (2,1)) with 95% confidence intervals for a single measure, absolute-agreement, 2-way mixed-effects model. In addition, Bland-Altman (BA) plots were used to visually inspect the measurement agreement. The values of the PB regression slope were close to 1 and intercept close to 0 for both step time and step length measures. The results obtained using ICC (2,1) for step length showed a moderate to excellent agreement for YA (between 0.81 and 0.95) and excellent agreement for PD (between 0.93 and 0.98), while both YA and PD had an excellent agreement in step time ICCs (>0.9). Finally, examining the BA plots showed that the measurement difference was within the limits of agreement (LoA) with a 95% probability. Results from this preliminary study indicate that using the SDI-Step algorithm to process signals from wearable IMUs provides measurements that are in close agreement with widely-used laboratory-based systems and can be considered as a valid tool for measuring spatiotemporal gait parameters.

Validity and sensitivity of instrumented postural and gait assessment using low-cost devices in Parkinson's disease.

BACKGROUND: Accurate assessment of balance and gait is necessary to monitor the clinical progress of Parkinson's disease (PD). Conventional clinical scales can be biased and have limited accuracy. Novel interactive devices are potentially useful to detect subtle posture or gait-related impairments. METHODS: Posturographic and single and dual-task gait assessments were performed to 54 individuals with PD and 43 healthy controls with the Wii Balance Board and the Kinect v2 and the, respectively. Individuals with PD were also assessed with the Tinetti Performance Oriented Mobility Assessment, the Functional Gait Assessment and the 10-m Walking Test. The influence of demographic and clinical variables on the performance in the instrumented posturographic and gait tests, the sensitivity of these tests to the clinical condition and phenotypes, and their convergent validity with clinical scales were investigated. RESULTS: Individuals with PD in H&Y I and I.5 stages showed similar performance to controls. The greatest differences in posture and gait were found between subjects in H&Y II.5 and H&Y I-I.5 stage, as well as controls. Dual-tasking enhanced the differences among all groups in gait parameters. Akinetic/rigid phenotype showed worse postural control and gait than other phenotypes. High significant correlations were found between the limits of stability and most of gait parameters with the clinical scales. CONCLUSIONS: Low-cost devices showed potential to objectively quantify posture and gait in established PD (H&Y ≥ II). Dual-tasking gait evaluation was more sensitive to detect differences among PD stages and compared to controls than free gait. Gait and posture were more impaired in akinetic/rigid PD.

A Novel Method for Gait Analysis on Center of Pressure Excursion Based on a Pressure-Sensitive Mat.

Center of pressure (COP) during gait is a useful measure for assessing gait ability and has been investigated using platform or insole systems. However, these systems have inherent restrictions in repeated measure design or in obtaining true vertical force. This study proposes a novel method based on a pressure-sensitive mat system for COP measurement and presents normal reference values for the system. To explore repeatability, this work also investigated relative and absolute intra-rater reliabilities and determined the number of footfalls required to obtain a reliable measurement. Ninety healthy young adults participated and performed barefoot walking on a force-sensitive mat at a comfortable and fast pace. The time points and subphase duration of the stance phase, displacement ranges, and mean locations of COP and velocity of COP excursion were parameterized. The results showed acceptable and consistent variabilities of the parameters. Seven footfalls were determined as the threshold for most parameters to show a good to reasonable level of reliability. In conclusion, the presented method can be used as a reliable measurement for COP excursion, and it is recommended that more than seven footfalls be collected to ensure a high level of reliability.