Gait Monitoring Research Articles

Determination of gait parameters in real‐world environment using low‐cost inertial sensors

AbstractThe ageing society causes an increasing need for gait monitoring in daily life to preserve the mobility of older people. Therefore, flexible low‐cost and easy‐to‐apply sensor networks are necessary. This study evaluates two in‐house developed inertial sensors for their capability of detecting spatial and temporal parameters of gait by using a time‐warping algorithm for stride segmentation. Thereby, impaired or wheeled walker supported gait can be analysed using a patient specific template. The results prove the capability to exclude sequences from the motion that do not correspond to strides and to detect spatio‐temporal parameters reliably. For the application to geriatric patients, however, further research is necessary.

Soft-Material-Based Smart Insoles for a Gait Monitoring System.

Spatiotemporal analysis of gait pattern is meaningful in diagnosing and prognosing foot and lower extremity musculoskeletal pathologies. Wearable smart sensors enable continuous real-time monitoring of gait, during daily life, without visiting clinics and the use of costly equipment. The purpose of this study was to develop a light-weight, durable, wireless, soft-material-based smart insole (SMSI) and examine its range of feasibility for real-time gait pattern analysis. A total of fifteen healthy adults (male: 10, female: 5, age 25.1 ± 2.64) were recruited for this study. Performance evaluation of the developed insole sensor was first executed by comparing the signal accuracy level between the SMSI and an F-scan. Gait data were simultaneously collected by two sensors for 3 min, on a treadmill, at a fixed speed. Each participant walked for four times, randomly, at the speed of 1.5 km/h (C1), 2.5 km/h (C2), 3.5 km/h (C3), and 4.5 km/h (C4). Step count from the two sensors resulted in 100% correlation in all four gait speed conditions (C1: 89 ± 7.4, C2: 113 ± 6.24, C3: 141 ± 9.74, and C4: 163 ± 7.38 steps). Stride-time was concurrently determined and R2 values showed a high correlation between the two sensors, in both feet (R2 ≥ 0.90, p < 0.05). Bilateral gait coordination analysis using phase coordination index (PCI) was performed to test clinical feasibility. PCI values of the SMSI resulted in 1.75 ± 0.80% (C1), 1.72 ± 0.81% (C2), 1.72 ± 0.79% (C3), and 1.73 ± 0.80% (C4), and those of the F-scan resulted in 1.66 ± 0.66%, 1.70 ± 0.66%, 1.67 ± 0.62%, and 1.70 ± 0.62%, respectively, showing the presence of a high correlation (R2 ≥ 0.94, p < 0.05). The insole developed in this study was found to have an equivalent performance to commercial sensors, and thus, can be used not only for future sensor-based monitoring device development studies but also in clinical setting for patient gait evaluations.

Self-powered gait pattern-based identity recognition by a soft and stretchable triboelectric band

Since each individual has distinct gait characteristics, monitoring human motion can enable identity recognition. Here, we report a self-powered band that can recognize human identity through gait pattern which is achieved by detecting muscle activity. The self-powered band is a soft and stretchable triboelectric nanogenerator (TENG) that is biocompatible and low-cost, which is looped around human body parts and generates electrical outputs in response to body motions involving muscle activities. The band can quantitatively detect walking step, speed and distance. Furthermore, the detected unique motion pattern of each individual allows the band to be used for identity recognition such as personal computer login and employee clock in through gait monitoring and analysis. This work opens new frontiers for the development of self-powered electronics and inspires new thoughts in human-machine interface.

Smart Shoe-Assisted Evaluation of Using a Single Trunk/Pocket-Worn Accelerometer to Detect Gait Phases.

Wearable sensors may enable the continuous monitoring of gait out of the clinic without requiring supervised tests and costly equipment. This paper investigates the use of a single wearable accelerometer to detect foot contact times and estimate temporal gait parameters (stride time, swing and stance duration). The experiments considered two possible body positions for the accelerometer: over the lower trunk and inside a trouser pocket. The latter approach could be implemented using a common smartphone. Notably, during the experiments, the ground truth was obtained by using a pair of sensorized shoes. Unlike ambient sensors and camera-based systems, sensorized shoes enable the evaluation of body-worn sensors even during longer walks. Experiments showed that both trunk and pocket positions achieved promising results in estimating gait parameters, with a mean absolute error below 50 ms.

Development of digital gait monitoring software for diagnosis of neuromuscular disorder

Development of digital gait monitoring software for diagnosis of neuromuscular disorder

PREDICTION OF THE SPATIO-TEMPORAL GAIT PARAMETERS USING INERTIAL SENSOR

Monitoring human gait is essential to quantify gait issues associated with fall-prone individuals as well as other gait-related movement disorders. Being portable and cost-effective, ambulatory gait analysis using inertial sensors is considered a promising alternative to traditional laboratory-based approach. The current study aimed to provide a method for predicting the spatio-temporal gait parameters using the wrist-worn inertial sensors. Eight young adults were involved in a laboratory study. Optical motion analysis system and force-plates were used for the assessment of baseline gait parameters. Spatio-temporal features of an Inertial Measurement Unit (IMU) on the wrist were analyzed. Multi-variate correlation analyses were performed to develop gait parameter prediction models. The results indicated that gait stride time was strongly correlated with peak-to-peak duration of wrist gyroscope signal in the anterio-posterior direction. Meanwhile, gait stride length was successfully predicted using a combination model of peak resultant wrist acceleration and peak sagittal wrist angle. In conclusion, current study provided the evidence that the wrist-worn inertial sensors are capable of estimating spatio-temporal gait parameters. This finding paves the foundation for developing a wrist-worn gait monitor with high user compliance.

A Triboelectric Nanogenerator‐Based Smart Insole for Multifunctional Gait Monitoring

AbstractAccurately monitoring human gait is critical for health evaluation and/or early diagnosis, especially for elder and injured people's healthcare. The presence of gait abnormalities could be important predictors of the risk of developing diseases. Herein, a triboelectric nanogenerator (TENG)‐based smart insole for real‐time gait monitoring is reported. Due to the novel air‐pressure‐driven structural design, the elastic TENG‐based sensors exhibit compelling features including simple fabrication, fast response time, high durability, and excellent mechanical robustness. The TENG‐based sensors can be easily integrated into the conventional insole so that it can convert the mechanical triggering/impact into electrical output. By analyzing such electrical signals, the smart insole could accurately monitor and distinguish various gait patterns in real time, including jump, step, walk, and run. The smart insole could also be used to monitor the abnormality of gait for rehabilitation assessment. In addition, the smart insole can play another important role in healthcare applications, for example, serving as a fall‐down alert system for elder people or patients. This work not only paves a new way for real‐time and long‐term gait monitoring, but also presents a new perspective for the practical applications of remote clinical biomotion analysis.

Piezoresistive nanocomposite films for foot strike data monitoring

It is important to understand the human gait especially for the patients under rehabilitation or in old age. Foot strike data is considered as the most important parameter to analyse gait. Piezoresponsive sensors are mostly used for this purpose. In this study, carbon nanofiber (CNF) based thermoplastic polyurethane (TPU) piezoresistive, nanocomposite films (15%, 20% and 25% w/w) were prepared, characterized and tested to study their suitability for such applications. Scanning electron microscope (SEM) studies on the cross section of nanocomposite films revealed that the CNF is well connected inside the TPU matrix. This well connected conducting network resulted in low bulk resistivity (98 ± 3.2 Ω-cm) and impedance (∼250 Ω). The compressive test results indicate more than threefold increase in strength due to CNF reinforcement. All the films showed excellent thickness recovery under cyclic loading while, 25% (w/w) CNF dispersed nanocomposite films’ showed more than 99% recovery after 100 cycles of cyclic compression. The samples also showed good dynamic mechanical stability from 1 to 10 Hz at body temperature (37 °C). The change in resistance under cyclic compression up to 70% strain was found to be linear with high correlation (R2 = 0.99). The gauge factor (up to 1.40 ± 0.03) shows little variation at different strain regions. A cyclic load setup was designed and fabricated to mimic the cyclic foot strike pattern of human gait. The nanocomposite films were tested at three different cyclic strain levels using the setup and the responses were monitored for 2 h using cathode ray oscilloscope (CRO). It has been observed that with the increase in CNF concentrations, the change in voltage goes up to 911.8 mV. The consistency of voltage response under cyclic compressive loading demonstrated in this work shows tremendous potential for these nanocomposite films to be successfully used for piezoresponsive applications of human gait monitoring.

THUR 229 Inertial sensors improve traditional gait monitoring in HSP patients

BackgroundHSP is characterised by spasticity and progressive gait impairment. There’s no reliable way to monitor gait deterioration during clinics. Optoelectronic systems have demonstrated differing characteristics between gait of HSP patients and controls. They’re expensive and impractical for use in clinic settings. Inertial sensors haven’t been used to characterise HSP gaitObjectivesStudy use of inertial sensors to identify gait characteristics that differentiate mild HSP patients from controls. To identify a gait based biomarker which can be used to monitor disease progression in a longitudinal study.MethodsNeurological examination, SPRS, Modified Ashworth score, brief pain inventory were undertaken. Instrumented timed up and go (iTUG) and instrumented 10 metre walk tests (i10) wearing an inertial sensor during clinic appointments at 6 month intervals.ResultsGait variables differentiating between patients and controls, including those with mild disease, were identified. Parameters differentiating between patients with SPG4 and SPG7 mutations were found. 8 patients were re-assessed after 6 months. Analysis did not show gait deterioration.ConclusionInertial sensors can detect differences between HSP patients and controls, including those mildly affected. They can also differentiate between patients with different mutations. Further follow up data is needed to assess whether inertial sensors can predict future gait deterioration.

Estimation of vertical walking ground reaction force in real-life environments using single IMU sensor

Monitoring natural human gait in real-life environments is essential in many applications, including quantification of disease progression, monitoring the effects of treatment, and monitoring alteration of performance biomarkers in professional sports. Walking ground reaction forces are among the key parameters necessary for gait analysis. However, these parameters are commonly measured using force plates or instrumented treadmills which are expensive and bulky and can only be used in a controlled laboratory environment. Despite the importance of real-life gait measurement, developing reliable and practical techniques and technologies necessary for continuous real-life monitoring of gait is still an open challenge, mainly due to the lack of a practical and cost-effective wearable technology for ground reaction force measurement. This paper presents a methodology to estimate the total walking ground reaction force GRFvt in the vertical direction using data from a single inertial measurement unit. Correlation analysis of the vertical acceleration of different body segments with GRFvt indicated that the 7th cervical vertebrae is one of the best locations for the sensor. The proposed method improves the accuracy of the state-of-the-art GRFvt estimation by 25%, by utilising the time-varying ratio of the vertical acceleration of the human body centre of mass and measured C7 vertical acceleration. Results of this study showed that the proposed method estimated consistently the GRFvt in both indoor and urban outdoor environment, with a 4–8% peak-to-peak normalised root mean square error.

Validation of a Wearable IMU System for Gait Analysis: Protocol and Application to a New System

Miniaturized wearable Inertial Measurement Units (IMU) offer new opportunities for the functional assessment of motor functions for medicine, sport, and ergonomics. Sparse reliability validation studies have been conducted without a common specific approach and protocol. A set of guidelines to design validation protocol for these systems is proposed hereafter. They are based on the comparison between video analysis and the gold standard optoelectronic motion capture system for Gait Analysis (GA). A setup of the protocol has been applied to a wearable device implementing an inertial measurement unit and a dedicated harmonic oscillator kinematic model of the center of mass. In total, 10 healthy volunteers took part in the study, and four trials of walking at a self-selected speed and step length have been simultaneously recorded by the two systems, analyzed, and compared blindly (40 datasets). The model detects the steps and the foot which supports body weight. The stride time and the cadence have a mean absolute percentage error of 5.7% and 4.9%, respectively. The mean absolute percentage error in the measurement of step’s length and step’s speed is 5.6% and 13.5%, respectively. Results confirm that the proposed methodology is complete and effective. It is demonstrated that the developed wearable system allows for a reliable assessment of human gait spatio-temporal parameters. Therefore, the goal of this paper is threefold. The first goal is to present and define structured Protocol Design Guidelines, where the related setup is implemented for the validation of wearable IMU systems particularly dedicated to GA and gait monitoring. The second goal is to apply these Protocol Design Guidelines to a case study in order to verify their feasibility, user-friendliness, and efficacy. The third goal is the validation of our biomechanical kinematic model with the gold standard reference.

Remote Monitoring in the Home Validates Clinical Gait Measures for Multiple Sclerosis.

Background: The timed 25-foot walk (T25FW) is widely used as a clinic performance measure, but has yet to be directly validated against gait speed in the home environment.Objectives: To develop an accurate method for remote assessment of walking speed and to test how predictive the clinic T25FW is for real-life walking.Methods: An AX3-Axivity tri-axial accelerometer was positioned on 32 MS patients (Expanded Disability Status Scale [EDSS] 0–6) in the clinic, who subsequently wore it at home for up to 7 days. Gait speed was calculated from these data using both a model developed with healthy volunteers and individually personalized models generated from a machine learning algorithm.Results: The healthy volunteer model predicted gait speed poorly for more disabled people with MS. However, the accuracy of individually personalized models was high regardless of disability (R-value = 0.98, p-value = 1.85 × 10−22). With the latter, we confirmed that the clinic T25FW is strongly predictive of the maximum sustained gait speed in the home environment (R-value = 0.89, p-value = 4.34 × 10−8).Conclusion: Remote gait monitoring with individually personalized models is accurate for patients with MS. Using these models, we have directly validated the clinical meaningfulness (i.e., predictiveness) of the clinic T25FW for the first time.

P 049 - Measuring gait parameters during community walking: Validation of a novel, low burden, long-lasting, free-living gait monitor

This paper describes the validation of an IMU-based gait monitor, developed for the long-term capture of gait parameters in the free-living community setting. Seven stroke survivors and 17 healthy participants performed a range of walking bouts upon a CAREN self-paced treadmill. The event timings of the device were compared to the gold-standard Vicon system and resulted in an average RMSE of 37.7 ms across all participants.

Comparison of Accelerometry-Based Features for Fall Risk Assessment Measured From Two Sensor Locations.

Falls are an unfortunate problem for older adults, their relatives and societies. Continuous gait monitoring for fall risk assessment during daily lives would allow early interventions to prevent injurious falls. Continuous gait monitoring is possible using technological solutions such as inertial sensors; for example accelerometers. Current solutions require attaching the sensor to a certain location on the body and many of them to the lower back, which is not convenient for the user. The objective of this study was to find out whether gait variables calculated from the acceleration signal measured during walk from two different locations on waist area differ from each other. Forty two older adult subjects were measured during walk test with a triaxial acceleration sensor worn on an elastic belt at the lower back and frontal hip area. Most of the analyzed gait features from the two locations have a strong correlation, indicating that these features are not sensitive to sensor location around waist level. A subsequent study is needed to confirm other locations for the sensors to allow analyzing gait during everyday lives.

Estimation of Tri-Axial Walking Ground Reaction Forces of Left and Right Foot from Total Forces in Real-Life Environments

Continuous monitoring of natural human gait in real-life environments is essential in many applications including disease monitoring, rehabilitation, and professional sports. Wearable inertial measurement units are successfully used to measure body kinematics in real-life environments and to estimate total walking ground reaction forces using equations of motion. However, for inverse dynamics and clinical gait analysis, the of each foot is required separately. Using an experimental dataset of 1243 tri-axial separate-foot time histories measured by the authors across eight years, this study proposes the ‘Twin Polynomial Method’ (TPM) to estimate the tri-axial left and right foot signals from the total signals. For each gait cycle, TPM fits polynomials of degree five, eight, and nine to the known single-support part of the left and right foot vertical, anterior-posterior, and medial-lateral signals, respectively, to extrapolate the unknown double-support parts of the corresponding signals. Validation of the proposed method both with force plate measurements (gold standard) in the laboratory, and in real-life environment showed a peak-to-peak normalized root mean square error of less than 2.5%, 6.5% and 7.5% for the estimated signals in the vertical, anterior-posterior and medial-lateral directions, respectively. These values show considerable improvement compared with the currently available decomposition methods in the literature.

Real-Life Measurement of Tri-Axial Walking Ground Reaction Forces Using Optimal Network of Wearable Inertial Measurement Units.

Monitoring natural human gait in real-life environment is essential in many applications including the quantification of disease progression, and monitoring the effects of treatment and alteration of performance biomarkers in professional sports. Nevertheless, reliable and practical techniques and technologies necessary for continuous real-life monitoring of gait is still not available. This paper explores in detail the correlations between the acceleration of different body segments and walking ground reaction forces GRF(t) in three dimensions and proposes three sensory systems, with one, two, and three inertial measurement units (IMUs), to estimate GRF(t) in the vertical (V), medial-lateral (ML), and anterior-posterior (AP) directions. The nonlinear autoregressive moving average model with exogenous inputs (NARMAX) non-linear system identification method was utilized to identify the optimal location for IMUs on the body for each system. A simple linear model was then proposed to estimate GRF(t) based on the correlation of segmental accelerations with each other. It was found that, for the three-IMU system, the proposed model estimated GRF(t) with average peak-to-peak normalized root mean square error (NRMSE) of 7%, 16%, and 18% in V, AP, and ML directions, respectively. With a simple subject-specific training at the beginning, these errors were reduced to 7%, 13%, and 13% in V, AP, and ML directions, respectively. These results were found favorably comparable with the results of the benchmark NARMAX model, with subject-specific training, with 0% (V), 4% (AP), and 1% (ML) NRMSE difference.

Biomechanical Gait Variable Estimation Using Wearable Sensors after Unilateral Total Knee Arthroplasty.

Total knee arthroplasty is a common surgical treatment for end-stage osteoarthritis of the knee. The majority of existing studies that have explored the relationship between recovery and gait biomechanics have been conducted in laboratory settings. However, seamless gait parameter monitoring in real-world conditions may provide a better understanding of recovery post-surgery. The purpose of this study was to estimate kinematic and kinetic gait variables using two ankle-worn wearable sensors in individuals after unilateral total knee arthroplasty. Eighteen subjects at least six months post-unilateral total knee arthroplasty participated in this study. Four biomechanical gait variables were measured using an instrumented split-belt treadmill and motion capture systems. Concurrently, eleven inertial gait variables were extracted from two ankle-worn accelerometers. Subsets of the inertial gait variables for each biomechanical gait variable estimation were statistically selected. Then, hierarchical regressions were created to determine the directional contributions of the inertial gait variables for biomechanical gait variable estimations. Selected inertial gait variables significantly predicted trial-averaged biomechanical gait variables. Moreover, strong directionally-aligned relationships were observed. Wearable-based gait monitoring of multiple and sequential kinetic gait variables in daily life could provide a more accurate understanding of the relationships between movement patterns and recovery from total knee arthroplasty.

Real-Time Gait Analysis Using a Single Head-Worn Inertial Measurement Unit

The background of this paper is to apply advanced real-time gait analysis to walking interventions in daily life setting. A vast of wearable devices provide gait information but not more than pedometer functions such as step counting, displacement, and velocity. This paper suggests a real-time gait analysis method based on a head-worn inertial measurement unit. A novel analysis method implements real-time detection of gait events (heel strike, toe off, and mid-stance phase) and immediately provides detailed spatiotemporal parameters. The reliability of this method was proven by a measurement with over 11 000 steps from seven participants on a 400-m outdoor track. The advanced gait analysis was conducted without any limitation of a fixed reference frame (e.g., indoor stage and infrared cameras). The mean absolute error in step-counting was 0.24%. Compared to a pedometer, additional gait parameters were obtained such as foot-ground contact time (CT) and CT ratio. The gait monitoring system can be used as real-time and long-term feedback, which is applicable in the management of the health status and on injury prevention.

Gait Shear and Plantar Pressure Monitoring: A Non-Invasive OFS Based Solution for e-Health Architectures.

In an era of unprecedented progress in sensing technology and communication, health services are now able to closely monitor patients and elderly citizens without jeopardizing their daily routines through health applications on their mobile devices in what is known as e-Health. Within this field, we propose an optical fiber sensor (OFS) based system for the simultaneous monitoring of shear and plantar pressure during gait movement. These parameters are considered to be two key factors in gait analysis that can help in the early diagnosis of multiple anomalies, such as diabetic foot ulcerations or in physical rehabilitation scenarios. The proposed solution is a biaxial OFS based on two in-line fiber Bragg gratings (FBGs), which were inscribed in the same optical fiber and placed individually in two adjacent cavities, forming a small sensing cell. Such design presents a more compact and resilient solution with higher accuracy when compared to the existing electronic systems. The implementation of the proposed elements into an insole is also described, showcasing the compactness of the sensing cells, which can easily be integrated into a non-invasive mobile e-Health solution for continuous remote gait monitoring of patients and elder citizens. The reported results show that the proposed system outperforms existing solutions, in the sense that it is able to dynamically discriminate shear and plantar pressure during gait.

Development of A Textile Capacitive Proximity Sensor and Gait Monitoring System for Smart Healthcare.

Gait is not only one of the most important functions and activities in daily life but is also a parameter to monitor one's health status. We propose a single channel capacitive proximity pressure sensor (TCPS) and gait monitoring system for smart healthcare. Insole-type TCPS (270mm in length) was designed consisting of three layers including two shield layers and a sensor layer. Analyzing the step count and stride time are the basic indicators in gait analysis, thus they were selected as evaluation indicators. A total of 12 subjects participated in the experiment to evaluate the resolution of our TCPS. To evaluate the accuracy of TCPS, step count and its error rates were simultaneously detected by naked eye, ZIKTO Walk (ZIKTO Co., Korea), and HJ-203-K pedometer (Omron Co., Japan) as reference. Results showed that the error rate of 1.77% in TCPS was lower than those of other devices and correlation coefficient was 0.958 (p-value = 0.000). ZIKTO Walk and pedometer do not provide information on stride time, therefore it was detected by F-scan (Tekscan, USA) to evaluate the performance of TCPS. As a result, error rate of stride time measured by TCPS was found to be 1% and the correlation coefficient was 0.685 (p-value=0.000). According to these results, our proposed system may be helpful in development of gait monitoring and measurement system as smart healthcare.