A Comparison of the Conventional PiG Marker Method Versus a Cluster-Based Model when recording Gait Kinematics in Trans-Tibial Prosthesis Users and the Implications for Future IMU Gait Analysis

Effects of Cognitive Load on Stride-to-Stride Variability During Walking Among Participants With and Without Functional Ankle Instability

BackgroundStudying the dynamics of nonlinear systems under dual-task conditions in people with functional ankle instability (FAI) can provide additional information about the variability of the structure of the system. However, how the cognitive load affects the stride-to-stride variability in the lower extremities throughout walking in those with and without FAI using nonlinear evaluation tools is unknown.MethodsEighteen volunteers with FAI and 19 healthy individuals were recruited for this study. The participants walked on a motor-driven treadmill at their preferred speed, with or without completing a cognitive task involving an auditory Stroop test. A passive retroreflective marker motion-capture system recorded the lower extremity gait kinematics in the sagittal, frontal, and transverse planes. The largest Lyapunov exponent (LyE) characterizes the variability of the temporal structure in walking patterns. Cognitive task performance was analyzed using error ratio and average reaction time in walking and sitting conditions.ResultsDuring walking with cognitive load, the group with FAI exhibited lower values for the LyE in knee kinematics in the sagittal plane than the normal walking (95% confidence interval [CI]: 0.44-0.92, P < .001). During normal walking (single task), participants with FAI demonstrated higher knee flexion-extension LyE (95% CI: 0.52-0.93, P = .04) than healthy people. The 2 groups had no significant differences regarding the LyE values for other kinematic variables (P > .05). Regarding cognitive performance, both FAI and healthy groups had remarkably longer reaction times (P < .001) while walking (mean ± SD: 0.92 ± 0.06) compared with the sitting condition (mean ± SD: 0.77 ± 0.03).ConclusionThe reduced variability observed in the walking patterns of individuals with FAI during walking with an added cognitive load suggests a compensatory mechanism due to sensorimotor constraints, highlighting the altered motor control strategies. Dual task and cognitive training may help correct these patterns and improve responses.Levels of Evidence:Level II.

A dataset of optical camera and IMU sensor derived kinematics of thirty transtibial prosthesis wearers

Motion analysis has played a crucial role in providing gait analysis for prosthetic users. Understanding kinematics in motion analysis allows for the evaluation of the effects of prostheses and the development of a prosthetic component design that aids in walking within the community. However, there are currently limited open datasets available to study the locomotion of individuals using transtibial prostheses, and most research studies involve a limited number of participants. This dataset shows 30 transtibial prosthesis users walking comfortably on a 10-meter walkway inside a laboratory. We offer a set of joint ankles obtained from the inertial measurement unit (IMU) signals in CSV file format. We also provide the optical motion capture (OMC) system's raw trajectory marker data in C3D file format and joint angle in CSV file format. This open dataset will provide resources for professionals interested in amputee gait for research in amputee gait detection and tracking algorithms. Moreover, the data will be the control data for comparison in developing advanced prosthetic components.

Validity and Reliability of Inertial Measurement Unit (IMU)-Derived 3D Joint Kinematics in Persons Wearing Transtibial Prosthesis

BACKGROUND

A validity and reliability assessment of inertial measurement unit (IMU)-derived joint angular kinematics during walking is a necessary step for motion analysis in the lower extremity prosthesis user population. This study aimed to assess the accuracy and reliability of an inertial measurement unit (IMU) system compared to an optical motion capture (OMC) system in transtibial prosthesis (TTP) users.

METHODS

Thirty TTP users were recruited and underwent simultaneous motion capture from IMU and OMC systems during walking. Reliability and validity were assessed using intra- and inter-subject variability with standard deviation (S.D.), average S.D., and intraclass correlation coefficient (ICC).

RESULTS

The intra-subject S.D. for all rotations of the lower limb joints were less than 1° for both systems. The IMU system had a lower mean S.D. (^o), as seen in inter-subject variability. The ICC revealed good to excellent agreement between the two systems for all sagittal kinematic parameters.

CONCLUSION

All joint angular kinematic comparisons supported the IMU system's results as comparable to OMC. The IMU was capable of precise sagittal plane motion data and demonstrated validity and reliability to OMC. These findings evidence that when compared to OMC, an IMU system may serve well in evaluating the gait of lower limb prosthesis users.

Wearable Sensor Clothing for Body Movement Measurement during Physical Activities in Healthcare

This paper presents a wearable wireless system for measuring human body activities, consisting of small inertial sensor nodes and the main hub for data transmission via Bluetooth for further analysis. Unlike optical and ultrasonic technologies, the proposed solution has no movement restrictions, such as the requirement to stay in the line of sight, and it provides information on the dynamics of the human body’s poses regardless of its location. The problem of the correct placement of sensors on the body is considered, a simplified architecture of the wearable clothing is described, an experimental set-up is developed and tests are performed. The system has been tested by performing several physical exercises and comparing the performance with the commercially available BTS Bioengineering SMART DX motion capture system. The results show that our solution is more suitable for complex exercises as the system based on digital cameras tends to lose some markers. The proposed wearable sensor clothing can be used as a multi-purpose data acquisition device for application-specific data analysis, thus providing an automated tool for scientists and doctors to measure patient’s body movements.