Accuracy Verification of Spatio-Temporal and Kinematic Parameters for Gait Using Inertial Measurement Unit System

Reliability and Construct Validity of a Mobile Application for the Finger Tapping Test Evaluation in People with Multiple Sclerosis

The finger tapping test (FTT) is a tool to evaluate the motor performance of the hand and fingers and eye-hand coordination with applicability in people with multiple sclerosis (pwMS). The aim of this study was to evaluate the intra- and inter-rater reliability of the TappingPro® mobile app and the construct validity between this app and validated clinical scales for motor performance in healthy subjects and pwMS. 42 healthy subjects (mean age 25.05) and 13 pwMS (mean age 51.69, EDSS between 3 and 7.5E) participated. FTT was performed with the TappingPro® mobile app. All participants were examined twice, with a one-week interval between the two appointments. For the analysis of construct validity, the Jamar® hydraulic hand dynamometer, Box and Blocks Test (BBT), and Nine Hole Peg Test (NHPT) were used. The intra-rater reliability showed a good correlation (Intraclass Correlation Coefficient, ICC > 0.787) for both upper limbs and both times of FTT for healthy subjects, and an excellent correlation (ICC > 0.956) for upper limbs and both times of FTT for pwMS. The ICC for the inter-rater reliability was good (ICC = 0.869) for the non-dominant upper limb in the FTT 10 s of the healthy subjects, and excellent (ICC > 0.904) for all the other measurements of the healthy subjects and pwMS. However, the Bland-Altman plots showed disagreement between observers and measurements that should be considered in the interpretation of clinical evaluations. The correlation analysis for healthy subjects showed poor associations between all variables, except for the association between hand grip strength and the FTT 60 s in the non-dominant upper limb, which had a moderate coefficient. For pwMS, there were moderate to excellent associations between BBT and the NHPT and FTT for both upper limbs. The correlations between hand grip strength and FFT were poor. This mobile app could be a useful and low-cost assessment tool in pwMS, allowing a simple evaluation and follow-up that has excellent correlation with clinical scales validated in this pathology.

Accuracy validation of a wearable IMU-based gait analysis in healthy female

OBJECTIVE

The aim of this study was to assess the accuracy and test-retest reliability of a wearable inertial measurement unit (IMU) system for gait analysis in healthy female compared to a gold-standard optoelectronic motion capture (OMC) system.

METHODS

In our study, we collected data from 5 healthy young females. Participants were attached with markers from both the OMC system and the IMU system simultaneously. Data was collected when participants walked on a 7 m walking path. Each participant performed 50 repetitions of walking on the path. To ensure the collection of complete gait cycle data, a gait cycle was considered valid only if the participant passed through the center of the walking path at the same time that the OMC system detected a valid marker signal. As a result, 5 gait cycles that met the standards of the OMC system were included in the final analysis. The stride length, cadence, velocity, stance phase and swing phase of the spatio-temporal parameters were included in the analysis. A generalized linear mixture model was used to assess the repeatability of the two systems. The Wilcoxon rank-sum test for continuous variables was used to compare the mean differences between the two systems. For evaluating the reliability of the IMU system, we calculated the Intra-class Correlation Coefficient (ICC). Additionally, Bland-Altman plots were used to compare the levels of agreement between the two systems.

RESULTS

The measurements of Spatio-temporal parameters, including the stance phase (P = 0.78, 0.13, L-R), swing phase (P = 0.78, 0.13, L-R), velocity (P = 0.14, 0.13, L-R), cadence (P = 0.53, 0.22, L-R), stride length (P = 0.05, 0.19, L-R), by the IMU system and OMC system were similar. Which suggested that IMU and OMC systems could be used interchangeably for gait measurements. The intra-rater reliability showed an excellent correlation for the stance phase, swing phase, velocity and cadence (Intraclass Correlation Coefficient, ICC > 0.9) for both systems. However, the correlation of stride length was poor (ICC = 0.36, P = 0.34, L) to medium (ICC = 0.56, P = 0.22, R). Additionally, the measurements of IMU systems were repeatable.

CONCLUSIONS

The results of IMU system and OMC system shown good repeatability. Wearable IMU system could analyze gait data accurately. In particular, the measurement of stance phase, swing phase, velocity and cadence showed excellent reliability. IMU system provided an alternative measurement to OMC for gait analysis. However, the measurement of stride length by IMU needs further consideration.

Design and validation of depth camera-based static posture assessment system

Postural abnormalities have become a prevalent issue affecting individuals of all ages, resulting in a diminished quality of life. Easy-use and reliable posture assessment tools can aid in screening for and correcting posture deviation at an early stage. In this study, we present a depth camera-based static posture assessment system to screen for common postural anomalies such as uneven shoulders, pelvic tilt, bowlegs and knock-knees, forward head, scoliosis, and shoulder blade inclination. The system consists of an Azure Kinect camera, a laptop, and evaluation software. Our system accurately measures skeleton and posture indexes and shows favorable agreement with a golden standard optical infrared motion capture system. The findings indicate that the system is a low-cost posture assessment tool with high precision and accuracy, suitable for initial screening of postural abnormalities in individuals of all ages.

Effect of Balance Training in Sitting Position Using Visual Feedback on Balance and Gait Ability in Chronic Stroke Patients

Chronic stroke often results in balance and gait impairments, significantly impacting patients' quality of life. The purpose of this study was to investigate whether the combined effect of unstable surface balance training and visual feedback, based on proprioceptive neuromuscular stimulation in patients with chronic stroke, is effective in restoring balance and gait ability. A total of 39 chronic stroke patients were randomly assigned to a visual feedback combined with unstable surface balance training group (VUSBG), an unstable surface balance training group (USBG), or a conventional physical therapy group (CG). This study was conducted with the Trunk Impairment Scale, the Bug Balance Scale, the Timed Get Up and Go Test, and Gait Analysis. VUSBG and USBG improved function and gait (stride length and hip/knee flexion angle), but there was no significant difference in the CG group. Specific results showed that the stride length in the VUSBG improved by 25% (p < 0.05), and the hip/knee flexion angle improved by 18% (p < 0.05). The post-hoc analysis revealed that VUSBG had a greater impact on the hip/knee flexion angle relative to the other two groups, as well as gait velocity and stride length relative to CG. Visual feedback complex exercise based on the principle of proprioceptive neuromuscular facilitation could be an intervention strategy to improve gait speed, trunk stability, and mobility in chronic stroke patients.

Evaluation of lower limb and pelvic marker placement precision among different evaluators and its impact on gait kinematics computed with the Conventional Gait Model

BACKGROUND

Gait analysis relies on the accurate and precise identification of anatomical landmarks to provide reliable and reproducible data. More specifically, the precision of marker placement among repeated measurements is responsible for increased variability in the output gait data.

RESEARCH QUESTION

The objective of this study was to quantify the precision of marker placement on the lower limbs by a test-retest procedure and to investigate its propagation to kinematic data.

METHODS

The protocol was tested on a cohort of eight asymptomatic adults involving four evaluators, with different levels of experience. Each evaluator performed, three repeated marker placements for each participant. The standard deviation was used to calculate the precision of the marker placement, the precision of the orientation of the anatomical (segment) coordinate systems, and the precision of the lower limb kinematics. In addition, one-way ANOVA was used to compare the intra-evaluator marker placement precision and kinematic precisions among the different levels of the evaluator's experience. Finally, a Pearson correlation between marker placement precision and kinematic precision was analyzed.

RESULTS

Results have shown a precision of skin markers within 10 mm and 12 mm for intra-evaluator and inter-evaluator, respectively. Analysis of kinematic data showed good to moderate reliability for all parameters apart from hip and knee rotation that demonstrated poor intra- and inter-evaluator precision. Inter-trial variability was observed reduced than intra- and inter-evaluator variability. Moreover, experience had a positive impact on kinematic reliability since evaluators with higher experience showed a statistically significant increase in precision for most kinematic parameters. However, no correlation was observed between marker placement precision and kinematic precision which indicates that an error in the placement of one specific marker can be compensated or enhanced, in a non-linear way, by an error in the placement of other markers.

Validity of artificial intelligence-based markerless motion capture system for clinical gait analysis: Spatiotemporal results in healthy adults and adults with Parkinson's disease

Markerless motion capture methods are continuously in development to target limitations encountered in marker-, sensor-, or depth-based systems. Previous evaluation of the KinaTrax markerless system was limited by differences in model definitions, gait event methods, and a homogenous subject sample. The purpose of this work was to evaluate the accuracy of spatiotemporal parameters in the markerless system with an updated markerless model, coordinate- and velocity-based gait events, and subjects representing young adult, older adult, and Parkinson's disease groups. Fifty-seven subjects and 216 trials were included in this analysis. Interclass correlation coefficients showed excellent agreement between the markerless system and a marker-based reference system for all spatial parameters. Temporal variables were similar, except swing time which showed good agreement. Concordance correlation coefficients were similar with all but swing time showing moderate to almost perfect concordance. Bland-Altman bias and limits of agreement (LOA) were small and improved from previous evaluations. Parameters showed similar agreement across coordinate- and velocity-based gait methods with the latter showing generally smaller LOAs. Improvements in spatiotemporal parameters in the present evaluation was due to inclusion of keypoints at the calcanei in the markerless model. Consistency in the calcanei keypoints relative to heel marker placements may improve results further. Similar to previous work, LOAs are within boundaries to detect differences in clinical groups. Results support the use of the markerless system for estimation of spatiotemporal parameters across age and clinical groups, but caution should be taken when generalizing findings due to remaining error in kinematic gait event methods.

Influence of the Hawthorne effect on spatiotemporal parameters, kinematics, ground reaction force, and the symmetry of the dominant and nondominant lower limbs during gait

The Hawthorne effect is a change in behavior resulting from awareness of being observed or evaluated. This study aimed to determine whether awareness of being evaluated or presence of an observer influence gait. Twenty-one young women were asked to walk in three conditions. In the first condition (unawareness of evaluation; UE), participants were aware that it was a practice trial, and there was no observer. In the second condition (awareness of evaluation; AE), participants were aware that their gait was being evaluated. The third condition (AE + researcher observation; RO) was similar to the second condition except that an additional researcher observed the participant' gait. The spatiotemporal, kinematic, ground reaction forces, and ratio index (symmetry of both lower limbs) were compared among the three conditions. A higher ratio index indicated a relative increase in the value on left versus right. Gait speed (P = 0.012) and stride length (right and left; P = 0.006 and 0.007, respectively) were significantly increased in the AE + RO than in UE. Range of motion of the right hip and left ankle was significantly greater in AE than in UE (P = 0.039 and 0.012, respectively). The ratio index of ground reaction force during push-off was significantly higher in AE and AE + RO conditions than in UE (P < 0.001 and P = 0.004, respectively). The Hawthorne effect (awareness of being evaluated or presence of an observer) potentially influences gait. Thus, factors that influence gait analysis should be considered when evaluating normal gait.

Effects of age-related changes in trunk and lower limb range of motion on gait

BACKGROUND

The ability to walk is crucial for maintaining independence and a high quality of life among older adults. Although gait characteristics have been extensively studied in older adults, most studies have investigated muscle activity in the joints of the trunk or the lower limbs without assessing their interactions. Thus, the causes of altered trunk and lower limb movement patterns in older adults remain to explore. Therefore, this study compared the joint kinematic parameters of both trunk and lower limbs between young and older adults to identify kinematic factors associated with changes in gait among older adults.

METHODS

In total, 64 older (32 males, aged 68.34 ± 7.38 years; 32 females, aged 67.16 ± 6.66 years) and 64 young (32 males, aged 19.44 ± 0.84 years; 32 females, aged 19.69 ± 0.86 years) healthy adults participated in this study. The range of motion (ROM) of the thorax, pelvis, and trunk in the horizontal plane and of the hip, knee, and ankle joints of the lower limbs in the sagittal plane were measured using a motion capture system with wearable sensors. Two-way analysis of variance assessed differences in ROM by group, sex, and spatio-temporal gait parameters; Pearson correlation analysis assessed the correlation of the trunk and lower limbs.

RESULTS

Step length, gait speed, and stride length were greater in young adults (p < 0.001) than in older adults, but older women displayed the fastest gait speed (p < 0.05). ROM values for the pelvis, thorax, trunk, knee joint, and ankle joint of young adults were greater (p < 0.05) than those in older adults. However, hip ROM in older adults was significantly greater than that in young adults (p < 0.05).

CONCLUSION

With increasing age, ROM of the lower limbs, especially the ankle joint, decreased significantly, resulting in a significant decrease in gait speed. As ROM of the pelvis decreased, stride length decreased significantly in older adults, who compensate through thoracic rotation. Thus, older adults should enhance muscle strength and increase ROM to improve gait patterns.

Validation of Inertial Sensors to Evaluate Gait Stability

The portability of wearable inertial sensors makes them particularly suitable for measuring gait in real-world walking situations. However, it is unclear how well inertial sensors can measure and evaluate gait stability compared to traditional laboratory-based optical motion capture. This study investigated whether an inertial sensor-based motion-capture suit could accurately assess gait stability. Healthy adult participants were asked to walk normally, with eyes closed, with approximately twice their normal step width, and in tandem. Their motion was simultaneously measured by inertial measurement units (IMU) and optical motion capture (Optical). Gait stability was assessed by calculating the margin of stability (MoS), short-term Lyapunov exponents, and step variability, along with basic gait parameters, using each system. We found that IMUs were able to detect the same differences among conditions as Optical for all but one of the measures. Bland-Altman and intraclass correlation (ICC) analysis demonstrated that mediolateral parameters (step width and mediolateral MoS) were measured less accurately by IMUs compared to their anterior-posterior equivalents (step length and anterior-posterior MoS). Our results demonstrate that IMUs can be used to evaluate gait stability through detecting changes in stability-related measures, but that the magnitudes of these measures might not be accurate or reliable, especially in the mediolateral direction.

Accuracy of the fully integrated Insole3's estimates of spatiotemporal parameters during walking

This study investigated the accuracy of the Insole3 wireless shoe device in estimating several clinically useful spatiotemporal parameters (STPs). Eleven subjects walked at slow (0.8-1.0 m/s) and moderate-paced (1.2-1.4 m/s) speeds. Data were simultaneously recorded using the Insole3 and an industry-standard, three-dimensional motion capture (MOCAP) system. An error analysis compared the resulting STP data from the two systems. The mean bias error (MBE) was generally lower for temporal variables, and somewhat higher, but acceptable, for spatial variables. The MBE for temporally-related cadence and cycle time were the lowest (less than ±0.45%), with 100% (110/110) of slow-paced walking trial values and 99.1% (109/110) of moderate-paced walking trial values within 5% of the MOCAP estimates. The MBE was highest for speed (3.23-4.91%) and stride length (3.68-4.63%), with between 52.7 and 69.1% of trial values falling within the 5% error range. Stance time and swing time ranged between -0.98 and 4.38% error for both walking conditions. The results of this study suggest that the Insole3 is a potential alternative to MOCAP for estimating several STPs, namely cadence, stance time, and cycle time, particularly for use outside of the laboratory setting.

Gait Analysis in Children with Cerebral Palsy: Are Plantar Pressure Insoles a Reliable Tool?

Cerebral palsy (CP) is a common cause of motor disability, and pedobarography is a useful, non-invasive, portable, and accessible tool; is easy to use in a clinical setting; and can provide plenty of information about foot–soil interaction and gait deviations. The reliability of this method in children with CP is lacking. The aim of this study is to investigate test–retest reliability and minimal detectable change (MDC) of plantar pressure insole variables in children with CP. Eight children performed two trials 8 ± 2.5 days apart, using foot insoles to collect plantar pressure data. Whole and segmented foot measurements were analyzed using intraclass correlation coefficients (ICC). The variability of the data was measured by calculating the standard error of measurement (SEM) and the MDC/ICC values demonstrated high test–retest reliability for most variables, ranging from good to excellent (ICC ≥ 0.60). The SEM and the MDC values were considered low for the different variables. The variability observed between sessions may be attributed to the heterogeneous sub-diagnosis of CP.

Recent Trends and Practices Toward Assessment and Rehabilitation of Neurodegenerative Disorders: Insights From Human Gait

The study of human movement and biomechanics forms an integral part of various clinical assessments and provides valuable information toward diagnosing neurodegenerative disorders where the motor symptoms predominate. Conventional gait and postural balance analysis techniques like force platforms, motion cameras, etc., are complex, expensive equipment requiring specialist operators, thereby posing a significant challenge toward translation to the clinics. The current manuscript presents an overview and relevant literature summarizing the umbrella of factors associated with neurodegenerative disorder management: from the pathogenesis and motor symptoms of commonly occurring disorders to current alternate practices toward its quantification and mitigation. This article reviews recent advances in technologies and methodologies for managing important neurodegenerative gait and balance disorders, emphasizing assessment and rehabilitation/assistance. The review predominantly focuses on the application of inertial sensors toward various facets of gait analysis, including event detection, spatiotemporal gait parameter measurement, estimation of joint kinematics, and postural balance analysis. In addition, the use of other sensing principles such as foot-force interaction measurement, electromyography techniques, electrogoniometers, force-myography, ultrasonic, piezoelectric, and microphone sensors has also been explored. The review also examined the commercially available wearable gait analysis systems. Additionally, a summary of recent progress in therapeutic approaches, viz., wearables, virtual reality (VR), and phytochemical compounds, has also been presented, explicitly targeting the neuro-motor and functional impairments associated with these disorders. Efforts toward therapeutic and functional rehabilitation through VR, wearables, and different phytochemical compounds are presented using recent examples of research across the commonly occurring neurodegenerative conditions [viz., Parkinson's disease (PD), Alzheimer's disease (AD), multiple sclerosis, Huntington's disease (HD), and amyotrophic lateral sclerosis (ALS)]. Studies exploring the potential role of Phyto compounds in mitigating commonly associated neurodegenerative pathologies such as mitochondrial dysfunction, α-synuclein accumulation, imbalance of free radicals, etc., are also discussed in breadth. Parameters such as joint angles, plantar pressure, and muscle force can be measured using portable and wearable sensors like accelerometers, gyroscopes, footswitches, force sensors, etc. Kinetic foot insoles and inertial measurement tools are widely explored for studying kinematic and kinetic parameters associated with gait. With advanced correlation algorithms and extensive RCTs, such measurement techniques can be an effective clinical and home-based monitoring and rehabilitation tool for neuro-impaired gait. As evident from the present literature, although the vast majority of works reported are not clinically and extensively validated to derive a firm conclusion about the effectiveness of such techniques, wearable sensors present a promising impact toward dealing with neurodegenerative motor disorders.

Using Sensor Graphs for Monitoring the Effect on the Performance of the OTAGO Exercise Program in Older Adults

The OTAGO exercise program is effective in decreasing the risk for falls of older adults. This research investigated if there is an indication that the OTAGO exercise program has a positive effect on the capacity and as well as on the performance in mobility. We used the data of the 10-months observational OTAGO pilot study with 15 (m = 1, f = 14) (pre-)frail participants aged 84.60 y (SD: 5.57 y). Motion sensors were installed in the flats of the participants and used to monitor their activity as a surrogate variable for performance. We derived a weighted directed multigraph from the physical sensor network, subtracted the weights of one day from a baseline, and used the difference in percent to quantify the change in performance. Least squares was used to compute the overall progress of the intervention (n = 9) and the control group (n = 6). In accordance with previous studies, we found indication for a positive effect of the OTAGO program on the capacity in both groups. Moreover, we found indication that the OTAGO program reduces the decline in performance of older adults in daily living. However, it is too early to conclude causalities from our findings because the data was collected during a pilot study.

Comparison of forward versus backward walking on spatiotemporal and kinematic parameters on sand: A preliminary study

The purpose of this study was to investigate the spatiotemporal and kinematic parameters of backward walking (BW) and forward walking (FW) on sand. Randomly selected subjects (n = 28) were categorized into a sand group (SG, n = 14) and an overground group (OG, n = 14). SG was directed to perform both FW and BW on sand, while OG performed the same on the overground. Spatiotemporal and kinematic parameters were measured using the LegSys + device. The comparative findings of both the groups showed that the spatiotemporal parameters of SG varied significantly from those of OG in both FW and BW conditions (p < 0.05). The kinematic parameters varied significantly between the two groups only in the FW condition (p < 0.05). When compared within each group, spatiotemporal and kinematic parameters in the BW condition were significantly different from those in the FW condition. However, the percentages of stance, swing, and double support were not significantly different between FW and BW conditions (p > 0.05). This study suggests that sand walking is associated with a different gait pattern than overground walking, as evident from the analysis of the results of spatiotemporal and kinematic parameters in both FW and BW conditions. Therefore, sand walking can be used as a new approach to gait and balance training in clinical practice.

Objectifying clinical gait assessment: using a single-point wearable sensor to quantify the spatiotemporal gait metrics of people with lumbar spinal stenosis

Background

Wearable accelerometer-containing devices have become a mainstay in clinical studies which attempt to classify the gait patterns in various diseases. A gait profile for lumbar spinal stenosis (LSS) has not been developed, and no study has validated a simple wearable system for the clinical assessment of gait in lumbar stenosis. This study identifies the changes to gait patterns that occur in LSS to create a preliminary disease-specific gait profile. In addition, this study compares a chest-based wearable sensor, the MetaMotionC^© device and inertial measurement unit python script (MMC/IMUPY) system, against a reference-standard, videography, to preliminarily assess its accuracy in measuring the gait features of patients with LSS.

Methods

We conduct a cross-sectional observational study examining the walking patterns of 25 LSS patients and 33 healthy controls. To construct a preliminary disease-specific gait profile for LSS, the gait patterns of the 25 LSS patients and 25 healthy controls with similar ages were compared. To assess the accuracy of the MMC/IMUPY system in measuring the gait features of patients with LSS, its results were compared with videography for the 21 LSS and 33 healthy controls whose walking bouts exceeded 30 m.

Results

Patients suffering from LSS walked significantly slower, with shorter, less frequent steps and higher asymmetry compared to healthy controls. The MMC/IMUPY system had >90% agreement with videography for all spatiotemporal gait metrics that both methods could measure.

Conclusions

The MMC/IMUPY system is a simple and feasible system for the construction of a preliminary disease-specific gait profile for LSS. Before clinical application in everyday living conditions is possible, further studies involving the construction of a more detailed disease-specific gait profile for LSS by disease severity, and the validation of the MMC/IMUPY system in the home environment, are required.

Wearable Sensor-Based Prediction Model of Timed up and Go Test in Older Adults

The Timed Up and Go (TUG) test has been frequently used to assess the risk of falls in older adults because it is an easy, fast, and simple method of examining functional mobility and balance without special equipment. The purpose of this study is to develop a model that predicts the TUG test using three-dimensional acceleration data collected from wearable sensors during normal walking. We recruited 37 older adults for an outdoor walking task, and seven inertial measurement unit (IMU)-based sensors were attached to each participant. The elastic net and ridge regression methods were used to reduce gait feature sets and build a predictive model. The proposed predictive model reliably estimated the participants' TUG scores with a small margin of prediction errors. Although the prediction accuracies with two foot-sensors were slightly better than those of other configurations (e.g., MAPE: foot (0.865 s) > foot and pelvis (0.918 s) > pelvis (0.921 s)), we recommend the use of a single IMU sensor at the pelvis since it would provide wearing comfort while avoiding the disturbance of daily activities. The proposed predictive model can enable clinicians to assess older adults' fall risks remotely through the evaluation of the TUG score during their daily walking.

Wearable Inertial Gait Algorithms: Impact of Wear Location and Environment in Healthy and Parkinson's Populations

Wearable inertial measurement units (IMUs) are used in gait analysis due to their discrete wearable attachment and long data recording possibilities within indoor and outdoor environments. Previously, lower back and shin/shank-based IMU algorithms detecting initial and final contact events (ICs-FCs) were developed and validated on a limited number of healthy young adults (YA), reporting that both IMU wear locations are suitable to use during indoor and outdoor gait analysis. However, the impact of age (e.g., older adults, OA), pathology (e.g., Parkinson's Disease, PD) and/or environment (e.g., indoor vs. outdoor) on algorithm accuracy have not been fully investigated. Here, we examined IMU gait data from 128 participants (72-YA, 20-OA, and 36-PD) to thoroughly investigate the suitability of ICs-FCs detection algorithms (1 × lower back and 1 × shin/shank-based) for quantifying temporal gait characteristics depending on IMU wear location and walking environment. The level of agreement between algorithms was investigated for different cohorts and walking environments. Although mean temporal characteristics from both algorithms were significantly correlated for all groups and environments, subtle but characteristically nuanced differences were observed between cohorts and environments. The lowest absolute agreement level was observed in PD (ICC_2,1 = 0.979, 0.806, 0.730, 0.980) whereas highest in YA (ICC_2,1 = 0.987, 0.936, 0.909, 0.989) for mean stride, stance, swing, and step times, respectively. Absolute agreement during treadmill walking (ICC_2,1 = 0.975, 0.914, 0.684, 0.945), indoor walking (ICC_2,1 = 0.987, 0.936, 0.909, 0.989) and outdoor walking (ICC_2,1 = 0.998, 0.940, 0.856, 0.998) was found for mean stride, stance, swing, and step times, respectively. Findings of this study suggest that agreements between algorithms are sensitive to the target cohort and environment. Therefore, researchers/clinicians should be cautious while interpreting temporal parameters that are extracted from inertial sensors-based algorithms especially for those with a neurological condition.

TRIPOD—A Treadmill Walking Dataset with IMU, Pressure-Distribution and Photoelectric Data for Gait Analysis

Inertial measurement units (IMUs) enable easy to operate and low-cost data recording for gait analysis. When combined with treadmill walking, a large number of steps can be collected in a controlled environment without the need of a dedicated gait analysis laboratory. In order to evaluate existing and novel IMU-based gait analysis algorithms for treadmill walking, a reference dataset that includes IMU data as well as reliable ground truth measurements for multiple participants and walking speeds is needed. This article provides a reference dataset consisting of 15 healthy young adults who walked on a treadmill at three different speeds. Data were acquired using seven IMUs placed on the lower body, two different reference systems (Zebris FDMT-HQ and OptoGait), and two RGB cameras. Additionally, in order to validate an existing IMU-based gait analysis algorithm using the dataset, an adaptable modular data analysis pipeline was built. Our results show agreement between the pressure-sensitive Zebris and the photoelectric OptoGait system (r = 0.99), demonstrating the quality of our reference data. As a use case, the performance of an algorithm originally designed for overground walking was tested on treadmill data using the data pipeline. The accuracy of stride length and stride time estimations was comparable to that reported in other studies with overground data, indicating that the algorithm is equally applicable to treadmill data. The Python source code of the data pipeline is publicly available, and the dataset will be provided by the authors upon request, enabling future evaluations of IMU gait analysis algorithms without the need of recording new data.

[Research progress on wearable physiological parameter monitoring and its clinical applications]

Wearable physiological parameter monitoring devices play an increasingly important role in daily health monitoring and disease diagnosis/treatment due to their continuous dynamic and low physiological/psychological load characteristics. After decades of development, wearable technologies have gradually matured, and research has expanded to clinical applications. This paper reviews the research progress of wearable physiological parameter monitoring technology and its clinical applications. Firstly, it introduces wearable physiological monitoring technology's research progress in terms of sensing technology and data processing and analysis. Then, it analyzes the monitoring physiological parameters and principles of current medical-grade wearable devices and proposes three specific directions of clinical application research: 1) real-time monitoring and predictive warning, 2) disease assessment and differential diagnosis, and 3) rehabilitation training and precision medicine. Finally, the challenges and response strategies of wearable physiological monitoring technology in the biomedical field are discussed, highlighting its clinical application value and clinical application mode to provide helpful reference information for the research of wearable technology-related fields.

A Deep Learning Approach for TUG and SPPB Score Prediction of (Pre-) Frail Older Adults on Real-Life IMU Data

Since older adults are prone to functional decline, using Inertial-Measurement-Units (IMU) for mobility assessment score prediction gives valuable information to physicians to diagnose changes in mobility and physical performance at an early stage and increases the chances of rehabilitation. This research introduces an approach for predicting the score of the Timed Up & Go test and Short-Physical-Performance-Battery assessment using IMU data and deep neural networks. The approach is validated on real-world data of a cohort of 20 frail or (pre-) frail older adults of an average of 84.7 years. The deep neural networks achieve an accuracy of about 95% for both tests for participants known by the network.

Test-Retest Reliability of a Conventional Gait Model for Registering Joint Angles during Initial Contact and Toe-Off in Healthy Subjects

The aim of this study was to evaluate the test-retest reliability of a conventional gait model (CGM), the Plug-in Gait model, to calculate the angles of the hip, knee, and ankle during initial contact (IC) and toe-off (TO). Gait analysis was performed using the Vicon Motion System^® (Oxford Metrics, Oxford, UK). The study group consisted of 50 healthy subjects. To evaluate the test-retest reliability, the intraclass correlation coefficient (ICC), the standard error of measurement (SEM), the minimal detectable change (MDC), and the Bland-Altman analysis with 95% limits of agreement were calculated. The ICC for the joint angles of the hip, knee, and ankle was higher than 0.80. However, the ankle angle at IC had an ICC lower than 0.80. The SEM was <5° for all parameters. The MDC was large (>5°) for the hip angle at IC. The Bland-Altman analysis indicated that the magnitude of divergence was between ±5° and ±9° at IC and around ±7° at TO. In conclusion, the ICC for the plug-in gait model was good for the hip, knee, and ankle angles during IC and TO. The plots revealed a disagreement between measurements that should be considered in patients' clinical assessments.

Machine-Learning Based Determination of Gait Events from Foot-Mounted Inertial Units

A promising but still scarcely explored strategy for the estimation of gait parameters based on inertial sensors involves the adoption of machine learning techniques. However, existing approaches are reliable only for specific conditions, inertial measurements unit (IMU) placement on the body, protocols, or when combined with additional devices. In this paper, we tested an alternative gait-events estimation approach which is fully data-driven and does not rely on a priori models or assumptions. High-frequency (512 Hz) data from a commercial inertial unit were recorded during 500 steps performed by 40 healthy participants. Sensors’ readings were synchronized with a reference ground reaction force system to determine initial/terminal contacts. Then, we extracted a set of features from windowed data labeled according to the reference. Two gray-box approaches were evaluated: (1) classifiers (decision trees) returning the presence of a gait event in each time window and (2) a classifier discriminating between stance and swing phases. Both outputs were submitted to a deterministic algorithm correcting spurious clusters of predictions. The stance vs. swing approach estimated the stride time duration with an average error lower than 20 ms and confidence bounds between ±50 ms. These figures are suitable to detect clinically meaningful differences across different populations.

Treadmill-to-Overground Mapping of Marker Trajectory for Treadmill-Based Continuous Gait Analysis

A treadmill was used to perform continuous walking tests in a limited space that can be covered by marker-based optical motion capture systems. Most treadmill-based gait data are analyzed based on gait cycle percentage. However, achieving continuous walking motion trajectories over time without time normalization is often required, even if tests are performed under treadmill walking conditions. This study presents a treadmill-to-overground mapping method of optical marker trajectories for treadmill-based continuous gait analysis, by adopting a simple concept of virtual origin. The position vector from the backward moving virtual origin to a targeted marker within a limited walking volume is the same as the position vector from the fixed origin to the forward moving marker over the ground. With the proposed method, it is possible (i) to observe the change in physical quantity visually during the treadmill walking, and (ii) to obtain overground-mapped gait data for evaluating the accuracy of the inertial-measurement-unit-based trajectory estimation. The accuracy of the proposed method was verified from various treadmill walking tests, which showed that the total travel displacement error rate was 0.32% on average.

Reliability of Kinovea® Software and Agreement with a Three-Dimensional Motion System for Gait Analysis in Healthy Subjects

Gait analysis is necessary to diagnose movement disorders. In order to reduce the costs of three-dimensional motion capture systems, new low-cost methods of motion analysis have been developed. The purpose of this study was to evaluate the inter- and intra-rater reliability of Kinovea^® and the agreement with a three-dimensional motion system for detecting the joint angles of the hip, knee and ankle during the initial contact phase of walking. Fifty healthy subjects participated in this study. All participants were examined twice with a one-week interval between the two appointments. The motion data were recorded using the VICON Motion System^® and digital video cameras. The intra-rater reliability showed a good correlation for the hip, the knee and the ankle joints (Intraclass Correlation Coefficient, ICC > 0.85) for both observers. The ICC for the inter-rater reliability was >0.90 for the hip, the knee and the ankle joints. The Bland-Altman plots showed that the magnitude of disagreement was approximately ±5° for intra-rater reliability, ±2.5° for inter-rater reliability and around ±2.5° to ±5° for Kinovea^® versus Vicon^®. The ICC was good for the hip, knee and ankle angles registered with Kinovea^® during the initial contact of walking for both observers (intra-rater reliability) and higher for the agreement between observers (inter-rater reliability). However, the Bland-Altman plots showed disagreement between observers, measurements and systems (Kinovea^® vs. three-dimensional motion system) that should be considered in the interpretation of clinical evaluations.