Do Cognitive Abilities Influence Physical and Mental Fatigue in Patients with Chronic Pain after Walking According to a Clinical Guideline for Physical Exercise?

The objective of this study is to explore the mediator role of cognitive fusion and chronic pain acceptance on the effects that the walking pattern, following an established clinical guideline for physical exercise, can have on fatigue (physical and mental) in patients with chronic pain. The sample consisted of a total of 231 women with fibromyalgia with a mean age of 56.91 years (Standard Deviation SD = 9.58 years, range 30−78 years). The results show a significant indirect effect of the walking pattern on both physical and mental fatigue through cognitive fusion and chronic pain acceptance. Specifically, walking predicted less cognitive fusion, which predicted greater chronic pain acceptance, which, in turn, predicted less mental and physical fatigue (Beta-B- = −0.04, Standard Error SE = 0.02, 95% Confidence Interval 95% CI = [−0.09, −0.02]; B = −0.09, SE = 0.05, 95% CI = [−0.22, −0,15], respectively). It can be concluded that the walking pattern is linked to both physical and mental fatigue through cognitive defusion and chronic pain acceptance. These cognitive abilities would allow fibromyalgia patients to perceive an improvement in both physical and mental fatigue by carrying out the walking pattern. Emphasizing the training of cognitive defusion and pain acceptance would improve the adherence of these patients to walking.

Vision Transformer-Based Tailing Detection in Videos

Tailing is defined as an event where a suspicious person follows someone closely. We define the problem of tailing detection from videos as an anomaly detection problem, where the goal is to find abnormalities in the walking pattern of the pedestrians (victim and follower). We, therefore, propose a modified Time-Series Vision Transformer (TSViT), a method for anomaly detection in video, specifically for tailing detection with a small dataset. We introduce an effective way to train TSViT with a small dataset by regularizing the prediction model. To do so, we first encode the spatial information of the pedestrians into 2D patterns and then pass them as tokens to the TSViT. Through a series of experiments, we show that the tailing detection on a small dataset using TSViT outperforms popular CNN-based architectures, as the CNN architectures tend to overfit with a small dataset of time-series images. We also show that when using time-series images, the performance of CNN-based architecture gradually drops, as the network depth is increased, to increase its capacity. On the other hand, a decreasing number of heads in Vision Transformer architecture shows good performance on time-series images, and the performance is further increased as the input resolution of the images is increased. Experimental results demonstrate that the TSViT performs better than the handcrafted rule-based method and CNN-based method for tailing detection. TSViT can be used in many applications for video anomaly detection, even with a small dataset.

Drift Control of Pedestrian Dead Reckoning (PDR) for Long Period Navigation under Different Smartphone Poses

The inherent errors of low-cost inertial sensors cause significant heading drift that accumulates over time, making it difficult to rely on Pedestrian Dead Reckoning (PDR) for navigation over a long period. Moreover, the flexible portability of the smartphone poses a challenge to PDR, especially for heading determination. In this work, we aimed to control the PDR drift under the conditions of the unconstrained smartphone to eventually enhance the PDR performance. To this end, we developed a robust step detection algorithm that efficiently captures the peak and valley events of the triggered steps regardless of the device’s pose. The correlation between these events was then leveraged as distinct features to improve smartphone pose detection. The proposed PDR system was then designed to select the step length and heading estimation approach based on a real-time walking pattern and pose discrimination algorithm. We also leveraged quasi-static magnetic field measurements that have less disturbance for estimating reliable compass heading and calibrating the gyro heading. Additionally, we also calibrated the step length and heading when a straight walking pattern is observed between two base nodes. Our results showed improved device pose recognition accuracy. Furthermore, robust and accurate results were achieved for step length, heading and position during long-term navigation under unconstrained smartphone conditions.

A novel instrumented outsole for real-time foot kinematics measurement: validation across different speeds and simulated foot landing in Cerebral Palsy gait

<pre>Neuromuscular disorders in Cerebral Palsy (CP) patients lead to foot deformities and affect foot biomechanics leading to compromised gait. Thus, measurement of the foot kinematic measurement is of particular interest to understand and characterize the walking pattern among CP patients. The objective of the present work is to develop a wearable instrument to measure foot kinematics such as foot-to-ground angle in three-dimensional planes and to measure the foot clearance i.e., toe and heel clearances. A template-based outsole was developed that incorporated an optical distance sensor located anatomically on the outsole and the magnetometer to measure the foot kinematics. The developed system was validated against the reference marker-based motion capture system (from Noraxon). The data from eight able-bodied participants were acquired simultaneously from both the systems (developed and the reference system) at three different walking speeds. A CoP based feedback was presented to the participants to shift the sagittal CoP anteriorly, posteriorly and normal to simulate the walking pattern of CP patients with three different foot landing strategies. Pearson's correlation coefficient of more than or equal to 0.62, root mean square error of less than or equal to 7.81 degrees and limit of agreement of more than or equal to 95% is found. Furthermore, a wireless wristband is developed and validated for real-time vibrotactile feedback. The measurement accuracy reported with outsole while participants simulated CP gait shows the potential of present work in real-time foot kinematics detection in CP patients. The instrumentation is wearable, low-cost, easy to use and implement.</pre>

A novel instrumented outsole for real-time foot kinematics measurement: validation across different speeds and simulated foot landing in Cerebral Palsy gait

<pre>Neuromuscular disorders in Cerebral Palsy (CP) patients lead to foot deformities and affect foot biomechanics leading to compromised gait. Thus, measurement of the foot kinematic measurement is of particular interest to understand and characterize the walking pattern among CP patients. The objective of the present work is to develop a wearable instrument to measure foot kinematics such as foot-to-ground angle in three-dimensional planes and to measure the foot clearance i.e., toe and heel clearances. A template-based outsole was developed that incorporated an optical distance sensor located anatomically on the outsole and the magnetometer to measure the foot kinematics. The developed system was validated against the reference marker-based motion capture system (from Noraxon). The data from eight able-bodied participants were acquired simultaneously from both the systems (developed and the reference system) at three different walking speeds. A CoP based feedback was presented to the participants to shift the sagittal CoP anteriorly, posteriorly and normal to simulate the walking pattern of CP patients with three different foot landing strategies. Pearson's correlation coefficient of more than or equal to 0.62, root mean square error of less than or equal to 7.81 degrees and limit of agreement of more than or equal to 95% is found. Furthermore, a wireless wristband is developed and validated for real-time vibrotactile feedback. The measurement accuracy reported with outsole while participants simulated CP gait shows the potential of present work in real-time foot kinematics detection in CP patients. The instrumentation is wearable, low-cost, easy to use and implement.</pre>

Biomechanical Evaluation of Prosthetic Feet Designed Using the Lower Leg Trajectory Error Framework

The walking pattern and comfort of a person with lower limb amputation are determined by the prosthetic foot’s diverse set of mechanical characteristics. However, most design methodologies are iterative and focus on individual parameters, preventing a holistic design of prosthetic feet for a user’s body size and walking preferences. Here we refined and evaluated the lower leg trajectory error (LLTE) framework, a novel quantitative and predictive design methodology that optimizes the mechanical function of a user’s prosthesis to encourage gait dynamics that match their body size and desired walking pattern. Five people with unilateral below-knee amputation walked over-ground at self-selected speeds using an LLTE-optimized foot made of Nylon 6/6, their daily-use foot, and a standardized commercial energy storage and return (ESR) foot. Using the LLTE feet, target able-bodied kinematics and kinetics were replicated to within 5.2% and 13.9%, respectively, 13.5% closer than with the commercial ESR foot. Additionally, energy return and center of mass propulsion work were 46% and 34% greater compared to the other two prostheses, which could lead to reduced walking effort. Similarly, peak limb loading and flexion moment on the intact leg were reduced by an average of 13.1%, lowering risk of long-term injuries. LLTE-feet were preferred over the commercial ESR foot across all users and preferred over the daily-use feet by two participants. These results suggest that the LLTE framework could be used to design customized, high performance ESR prostheses using low-cost Nylon 6/6 material.

Interpretation of Gait Supervising Mechanism Using Sensor Integrated Makeshift and Analysing Pattern by K-Means Clustering Algorithm

Human mobility or walking pattern(gait) is described as the interpreter movements of the rotatory body to achieve extensive range of locomotion. Gait analysis is foremost widely used technique for identifying abnormalities in the lower extremities and gait characteristics essentially support HAT (Head, Arm & Trunk). The act of walking is unconscious when there are no dysfunctions, but for ambulated the continuous monitoring is required. The existing clinical analysis method couldn’t achieve the daily walking routine within the confinement of a room.The proposed method focuses on developing an ambulatory system on daily routines by incorporating feasible techniques for achieving the gait pattern which is not confined to a room atmosphere where all possibilities of walking pattern can’t be reached.This system has expounded an ideology, to interpret the gait parameters using an insole type shoe integrated sensor system. Here, a wearable gait system which is incorporated with force resistive sensors, piezo sensors, inertial sensors and IR sensors are interfaced to the ESP 32. The corresponding sensors extract the data of kinematic angles, kinetics, foot pressure, step count and foot stride investigations.The system proved to be efficient in finding the phases and orientation of the individual by interpreting values from the device. Acquired data can be clustered together to find the abnormal and normal values by applying K-Means clustering algorithm, later the values are utilized in biomechanics for rectifying posture or movement related problems.The device will have several applications in sports, rehabilitation medicine and post-surgery treatment.