Modular soft pneumatic actuator mimics elephant trunk locomotion.

Robots support and facilitate tasks in all life fields. Soft robots specifically have the advantages of inherent compliance, safe interaction and flexible deformability. Soft pneumatic network (Pneu-Net) is a soft pneumatic actuator (SPA) composed of network of chambers that is actuated by pneumatic power. Soft Pneu-Net fits the human interface applications perfectly. In this paper, a bio-inspired modular based design for Pneu-Net actuator is developed. The actuator mimics the elephant trunk curling to be employed for rehabilitation of human hand fingers. The actuator is an integrated four Pneu-Net modules actuator which is attached to hand's finger. The main introduced advantages in the new developed actuator are: providing four degrees of freedom (DoF) essential for finger's motion by single compound actuator and developing a methodology for a modular soft Pneu-Net actuator that is efficiently reproducible. The actuator's design is developed using computer aided design (CAD) software SOLIDWORKS. The design is simulated using finite element modeling (FEM) software ABAQUS. Fabrication process uses 3D printed molds. Soft material is molded in the 3D printed molds, forming actuator's modules. Actuator's modules are integrated by adhesion using the soft material. A proposed non-standard hyper-elastic material biaxial tension test is introduced as a quick material properties identification method that can produce a test table used for material identification in the FEM. Enhanced version for the actuator uses reinforcement fibers. Results show advances for the reinforced actuator, as it limits the unwanted actuator's strain and deformation. The reinforced actuator shows improved energy efficiency reaches to 46%.

Role of virtual reality in examining the effect of fear of falling (FOF) on postural stability in individuals without and with Parkinson's disease in Egypt: a mixed-methods feasibility study protocol.

BACKGROUND: Falls are common in older people and individuals with neurological conditions. Parkinson's disease (PD) is known for postural instability causing mobility disabilities, falls and reduced quality of life. The fear of falling (FOF), a natural response to unstable balance, can worsen postural control problems. Evaluating FOF relies largely on affected persons' subjective accounts due to limited objective assessment methods available. The aim of this mixed-methods feasibility study is to develop an assessment method for FOF while in motion and walking within virtual environments. This study will assess a range of FOF-related responses, including cognitive factors, neuromuscular response and postural stability. METHODS AND ANALYSIS: This feasibility study will consist of four phases: the first two phases will include people without PD, while the other two will include people diagnosed with PD. Participants will be assessed for direct and indirect responses to real life, as well as virtual environment walking scenarios that may induce FOF. Data from questionnaires, different neurophysiological assessments, movement and gait parameters, alongside evaluations of usability and acceptability, will be collected. Semistructured interviews involving both participants and research assistants shall take place to elicit their experiences throughout different phases of the assessments undertaken. Demographic data, the scores of assessment scales, as well as feasibility, usability and acceptability of the measurement methods, will be illustrated via descriptive statistics. Movement and gait outcomes, together with neurophysiological data, will be extracted and calculated. Exploring relationships between different factors in the study will be achieved using a regression model. Thematic analysis will be the approach used to manage qualitative data. ETHICS AND DISSEMINATION: This feasibility study was approved by the Ethics Committee of the Faculty of Physical Therapy, Kafr El Sheikh University, Egypt (number: P.T/NEUR/3/2023/46). The results of this study will be published in a peer-reviewed journal. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov Registry (NCT05931692).

Sensor positioning for a human activity recognition system using a double layer classifier.

This paper describes the development of a human gait activity recognition system. A multi-sensor recognition system, which has been developed for this purpose, was reduced to a single sensor-based recognition system. A sensor election method was devised based on the maximum relevance minimum redundancy feature selector to determine the sensor's optimum position regarding activity recognition. The election method proved that the thigh has the highest contribution to recognize walking, stairs and ramp ascending, and descending activities. A recognition algorithm (which depends mainly on features that are classified by random forest, and selected by a combined feature selector using the maximum relevance minimum redundancy and genetic algorithm) has been modified to compensate the degradation that occurs in the prediction accuracy due to the reduction in the number of sensors. The first modification was implementing a double layer classifier in order to discriminate between the interfered activities. The second modification was adding physical features to the features dictionary used. These modifications succeeded to improve the prediction accuracy to allow a single sensor recognition system to behave in the same manner as a multi-sensor activity recognition system.

Prediction of human gait activities using wearable sensors.

This paper aims to enhance the accuracy of human gait prediction using machine learning algorithms. Three classifiers are used in this paper: XGBoost, Random Forest, and SVM. A predefined dataset is used for feature extraction and classification. Gait prediction is determined during several locomotion activities: sitting (S), level walking (LW), ramp ascend (RA), ramp descend (RD), stair ascend (SA), stair descend (SD), and standing (ST). The results are gained for steady-state (SS) and overall (full) gait cycle. Two sets of sensors are used. The first set uses inertial measurement units only. The second set uses inertial measurement units, electromyography, and electro-goniometers. The comparison is based on prediction accuracy and prediction time. In addition, a comparison between the prediction times of XGBoost with CPU and GPU is introduced due to the easiness of using XGBoost with GPU. The results of this paper can help to choose a classifier for gait prediction that can obtain acceptable accuracy with fewer types of sensors.

Soft Finger Modelling and Co-Simulation Control towards Assistive Exoskeleton Hand Glove.

The soft pneumatic actuators of an assistive exoskeleton hand glove are here designed. The design of the actuators focuses on allowing the actuator to perform the required bending and to restrict elongation or twisting of the actuator. The actuator is then modeled using ABAQUS/CAE, a finite element modeling software, and the open loop response of the model is obtained. The parameters of the actuator are then optimized to reach the optimal parameters corresponding to the best performance. Design of experiment (DOE) techniques are then approached to study the robustness of the system. Software-in-the-loop (SiL) is then approached to control the model variables via a proportional-integral-derivative (PID) control generated by FORTRAN code. The link between the two programs is to be achieved by the user subroutine that is written, where the subroutine receives values from ABAQUS/CAE, performs calculations, and passes values back to the software. The controller's parameters are tuned and then the closed loop response of the model is obtained by setting the desired bending angle and running the model. Furthermore, a concentrated force at the tip of the actuator is added to observe the actuator's response to external disturbance.

Foot trajectories and loading rates in a transfemoral amputee for six different commercial prosthetic knees: An indication of adaptability.

BACKGROUND: The relationship between the functional loading rate and heel velocities was assessed in an active unilateral transfemoral amputee (UTFA) for adaptation to six different commercial prosthetic knees. OBJECTIVE: To Investigate the short-term process of adaptability for UTFA for two types of prosthetic knees were evaluated, based on the correlation between heel vertical velocity and transient loading rate. METHODS: The loading rate was calculated from the slope of ground reaction forces (GRF) and the corresponding time. The heel velocities and GRF were obtained by a motion analysis system. RESULTS: Biomechanical adaptation was evident following a short period of prosthetic knee use based upon the mean transient impact (loading rate) and the heel vertical velocity in slow, normal and fast walking. Trend lines of transient impact versus vertical heel velocity for a set of actively controlled variable damping (microprocessor) and mechanically passive prosthetic knees were all negatively correlated, except for an amputated leg during normal pace and healthy leg during fast pace. For an amputee to adapt well to a prescribed prosthesis excellent coordination between the intact and amputated limbs is required to control placement of the amputated leg to achieve a gait comparable to healthy subjects. CONCLUSION: There are many factors such as the hip, knee flexion/extension and the ankle plantarflexion/dorsiflexion contributing to the control of the transient impact of an amputee during walking. Therefore, for enhanced control of a prosthetic knee, a multifaceted approach is required. This study showed that UTFA adaption to different prosthetic knees in the short term with slower than self-selected speed is completely achievable based on the negative correlation of ground reaction forces versus linear velocity. Reduced speed may provide the prosthetists with the vision of the amputees' progression of adaptation with a newly prescribed prosthetic knee.

Investigation into reducing anthropomorphic hand degrees of freedom while maintaining human hand grasping functions.

Underactuation is widely used when designing anthropomorphic hand, which involves fewer degrees of actuation than degrees of freedom. However, the similarities between coordinated joint movements and movement variances across different grasp tasks have not been suitably examined. This work suggests a systematic approach to identify the actuation strategy with the minimum number for degrees of actuation for anthropomorphic hands. This work evaluates the correlations of coordinated movements in human hands during 23 grasp tasks to suggest actuation strategies for anthropomorphic hands. Our approach proceeds as follows: first, we find the best description for each coordinated joint movement in each grasp task by using multiple linear regression; then, based on the similarities between joint movements, we classify hand joints into groups by using hierarchical cluster analysis; finally, we reduce the dimensionality of each group of joints by employing principal components analysis. The metacarpophalangeal joints and proximal interphalangeal joints have the best and most consistent description of their coordinated movements across all grasp tasks. The thumb metacarpophalangeal and abduction/adduction between the ring and little fingers exhibit relatively high independence of movement. The distal interphalangeal joints show a high degree of independent movement but not for all grasp tasks. Analysis of the results indicates that for the distal interphalangeal joints, their coordinated movements are better explained when all fingers wrap around the object. Our approach fails to provide more information for the other joints. We conclude that 19 degrees of freedom for an anthropomorphic hand can be reduced to 13 degrees of actuation distributed between six groups of joints. The number of degrees of actuation can be further reduced to six by relaxing the dimensionality reduction criteria. Other resolutions are as follows: (a) the joint coupling scheme should be joint-based rather than finger-based and (b) hand designs may need to include finger abduction/adduction movements.

Prediction of gait events in walking activities with a Bayesian perception system.

In this paper, a robust probabilistic formulation for prediction of gait events from human walking activities using wearable sensors is presented. This approach combines the output from a Bayesian perception system with observations from actions and decisions made over time. The perception system makes decisions about the current gait events, while observations from decisions and actions allow to predict the most probable gait event during walking activities. Furthermore, our proposed method is capable to evaluate the accuracy of its predictions, which permits to obtain a better performance and trade-off between accuracy and speed. In our work, we use data from wearable inertial measurement sensors attached to the thigh, shank and foot of human participants. The proposed perception system is validated with multiple experiments for recognition and prediction of gait events using angular velocity data from three walking activities; level-ground, ramp ascent and ramp descent. The results show that our method is fast, accurate and capable to evaluate and adapt its own performance. Overall, our Bayesian perception system demonstrates to be a suitable high-level method for the development of reliable and intelligent assistive and rehabilitation robots.

Virtual prototyping of a semi-active transfemoral prosthetic leg.

This article presents a virtual prototyping study of a semi-active lower limb prosthesis to improve the functionality of an amputee during prosthesis-environment interaction for level ground walking. Articulated ankle-foot prosthesis and a single-axis semi-active prosthetic knee with active and passive operating modes were considered. Data for level ground walking were collected using a photogrammetric method in order to develop a base-line simulation model and with the hip kinematics input to verify the proposed design. The simulated results show that the semi-active lower limb prosthesis is able to move efficiently in passive mode, and the activation time of the knee actuator can be reduced by approximately 50%. Therefore, this semi-active system has the potential to reduce the energy consumption of the actuators required during level ground walking and requires less compensation from the amputee due to lower deviation of the vertical excursion of body centre of mass.