ReachMAN to help sub-acute patients training reaching and manipulation

Conventional rehabilitation after stroke, consisting in one-to-one practice with the therapist, is labor-intensive and subjective. Furthermore, there is evidence that increasing training would benefit the motor function of stroke survivors, though the available resources do not allow it. Training with dedicated robotic devices promises to address these problems and to promote motivation through therapeutic games. The goal of this project is to develop a simple robotic system to assist rehabilitation that could easily be integrated in existing hospital environments and rehabilitation centers. A study was first carried out to analyze the kinematics of hand movements while performing representative activities of daily living. Results showed that movements were confined to one plane so can be trained using a robot with less degrees-of-freedom (DOF). Hence ReachMAN, a compact 3 DOF robot based on an endpoint based approach, was developed to train reaching, forearm pronosupination and grasping, independently or simultaneously. ReachMAN's exercises were developed using games based on software thereby facilitating active participation from patients. Visual, haptic and performance feedback were provided to increase motivation. Tuneable levels of difficulty were provided to suit patient's ability. A pilot study with three subjects was first conducted to evaluate the potential use of ReachMAN as a rehabilitation tool and to determine suitable settings for training. Following positive results from a pilot study, a clinical study was initiated to investigate the effect of rehabilitation using ReachMAN. Preliminary results of 6 subjects show an increase in patients upper limb motor activity, range of movements, smoothness and reduction in movement duration. Subjects reported to be motivated with the robot training and felt that the robot helped in their recovery. The results of this thesis suggest that a compact and simple robot such as ReachMAN can be used to enhance recovery in sub-acute stroke patients

A review of sensor technology and sensor fusion methods for map-based localization of service robot

Service robot is currently gaining traction, particularly in hospitality, geriatric care and healthcare industries. The navigation of service robots requires high adaptability, flexibility and reliability. Hence, map-based navigation is suitable for service robot because of the ease in updating changes in environment and the flexibility in determining a new optimal path. For map-based navigation to be robust, an accurate and precise localization method is necessary. Localization problem can be defined as recognizing the robot’s own position in a given environment and is a crucial step in any navigational process. Major difficulties of localization include dynamic changes of the real world, uncertainties and limited sensor information. This paper presents a comparative review of sensor technology and sensor fusion methods suitable for map-based localization, focusing on service robot applications

A Simple Position Sensing Device for Upper Limb Rehabilitation

Stroke is a leading cause of disability which can affect shoulder and elbow movements which are necessary for reaching activities in numerous daily routines. Rehabilitation under the supervision of physiotherapists in healthcare settings is to encourage the recovery process. Unfortunately, these sessions are often labor-intensive and limited intervention time between physiotherapist and the stroke patient due to staff constraints. Dedicated robotic devices have been developed to overcome this issues. However, the high cost of these robots is a major concern as it limits their cost-benefit profiles, thus impeding large scale implementation. This paper presents a simple and portable unactuated interactive rehabilitation device for upper limb rehabilitation purposes. This device has been developed by using a conventional mouse integrated with three interactive training modules, namely the Triangle, Square, and Circle modules intended for training shoulder and elbow movements. Results from five healthy subjects showed the more deviation from the path will be happened when the subject move their hand to the other side of their dominant hand. Besides, the shape of the module that includes combination of X and Y axis direction is more difficult compare to either X or Y axis

Neural-Network Based Adaptive Proxemics-Costmap for Human-Aware Autonomous Robot Navigation

In the revolution of Industry 4.0, autonomous robot navigation plays a vital role in ensuring intelligent cooperation with human workers to increase manufacturing efficiency. Human prefers to maintain a proxemic distance with other subjects for safety and comfort purposes, where the human personal-space can be represented by a costmap. Current proxemic costmaps perform well in defining the proxemic boundary to maintain the human-robot proxemic distance. However, these approaches generate static costmaps that are not adaptive towards different human states (linear position, angular position and velocity). This problem impacts the robot navigation efficiency, reduces human safety and comfort as the autonomous robot failed to prioritize avoiding certain humans over the other. To overcome this drawback, this paper proposed a neural-network based adaptive proxemic-costmap, named as NNPC, that can generate different sized personal-spaces at different human state encounters. The proposed proxemic-costmap was developed by learning a neural-network model using real human state data. A total of three human scenarios were used for data collection. The data were collected by tracking the humans in video recordings. After the model was trained, the proposed NNPC costmap was evaluated against two other state-of-art proxemic costmaps in five simulated human scenarios with various human states. Results show that NNPC outperformed the compared costmaps by ensuring human-aware robot manoeuvres that have higher robot efficiency and increased human safety and comfort. &nbsp

Multiple Linear Regression in Predicting Motor Assessment Scale of Stroke Patients

The Multiple Linear Regression (MLR) is a predictive model that was commonly used to predict the clinical score of stroke patients. However, the performance of the predictive model slightly depends on the method of feature selection on the data as input predictor to the model. Therefore, appropriate feature selection method needs to be investigated in order to give an optimum performance of the prediction. This paper aims (i) to develop predictive model for Motor Assessment Scale (MAS) prediction of stroke patients, (ii) to establish relationship between kinematic variables and MAS score using a predictive model, (iii) to evaluate the prediction performance of a predictive model based on root mean squared error (RMSE) and coefficient of determination R2. Three types of feature selection methods involve in this study which are the combination of all kinematic variables, the combination of the best four or less kinematic variables, and the combination of kinematic variables based on p < 0.05. The prediction performance of MLR model between two assessment devices (iRest and ReHAD) has been compared. As the result, MLR model for ReHAD with the combination of kinematic variables that has p < 0.05 as input predictor has the best performance with Draw I (RMSEte = 1.9228, R2 = 0.8623), Draw Diamond (RMSEte = 2.6136, R2 = 0.7477), and Draw Circle (RMSEte = 2.1756, R2 = 0.8268). These finding suggest that the relationship between kinematic variables and MAS score of stoke patients is strong, and the MLR model with feature selection of kinematic variables that has p < 0.05 is able to predict the MAS score of stroke patients using the kinematic variables extracted from the assessment device

Adaptive Phototransistor Sensor for Line Finding

AbstractLine finding is used by wheeled mobile robot for localization. A phototransistor array was designed to detect the line position relative to the robot. This sensor is composed of six phototransistors to detect the position of line on the floor relative to the wheeled mobile robot. Because the ambience may change with time and the floor colour may be different from one location to another, an adaptive scheme has been designed to find the line on the floor. This proposed scheme consists of three parts; modulation and demodulation, threshold recognition with k-means clustering, and line finding with fuzzy logic. Modulation and demodulation technique is used to tackle the problem of different ambience in the surrounding. K-means clustering is used to recognize the contrast in the colour of line and floor while fuzzy logic is used to find the line relative to the sensor. Experiments were conducted in a microcontroller and it was found out that this scheme can find the line on the floor with minimum error

Investigation of sensor-based quantitative model for badminton skill analysis and assessment

Badminton is one of the most popular sports in Malaysia. The main aim of this project is to investigate sets of movements in badminton training using sensors, to identify the good movement that enhance badminton performance. In addition, this project also aims to identify measurable parameters to quantify badminton skill levels. The performance of elite players will be studied to identify benchmark values for these measurable parameters. A quantitative model will be proposed using these measurable parameters to help in the objective assessment of skill levels. Findings of this project will help badminton players to improve their techniques, as well as providing an objective measurement to assess badminton skills

A review on energy efficiency in autonomous mobile robots

Purpose: This paper aims to provide a comprehensive analysis of the state of the art in energy efficiency for autonomous mobile robots (AMRs), focusing on energy sources, consumption models, energy-efficient locomotion, hardware energy consumption, optimization in path planning and scheduling methods, and to suggest future research directions. Design/methodology/approach: The systematic literature review (SLR) identified 244 papers for analysis. Research articles published from 2010 onwards were searched in databases including Google Scholar, ScienceDirect and Scopus using keywords and search criteria related to energy and power management in various robotic systems. Findings: The review highlights the following key findings: batteries are the primary energy source for AMRs, with advances in battery management systems enhancing efficiency; hybrid models offer superior accuracy and robustness; locomotion contributes over 50% of a mobile robot’s total energy consumption, emphasizing the need for optimized control methods; factors such as the center of mass impact AMR energy consumption; path planning algorithms and scheduling methods are essential for energy optimization, with algorithm choice depending on specific requirements and constraints. Research limitations/implications: The review concentrates on wheeled robots, excluding walking ones. Future work should improve consumption models, explore optimization methods, examine artificial intelligence/machine learning roles and assess energy efficiency trade-offs. Originality/value: This paper provides a comprehensive analysis of energy efficiency in AMRs, highlighting the key findings from the SLR and suggests future research directions for further advancements in this field

Force variability as an objective measure of surgical skill

This study investigated the force variability of subjects with different level of surgical skills for different force levels. Twelve participants were recruited from three different levels of surgical experiences: A group of surgeon (N = 4), medical student (N = 3) and engineering student (N = 5) underwent a simple finger force control task using a custom developed ‘Force Matching’ module. Three different levels of target force were used: 2 N, 4 N, and 6 N. The task was performed simultaneously using right and left hands. The mean error of force was measured to compare the performance between the three group using Kruskal-Wallis test. A statistically significant difference was detected among the three groups at 2 N when using right hand. We also found that the surgeon group made less error compared to the two other groups at force level 4 N and 6 N for both hands. This finding has important implication for developing a parametric assessment model to evaluate basic skill level in surgical procedures. However, for most accurate result, a big sample size of subject is require

Prediction of ventricular fibrillation using support vector machine

Sudden cardiac death (SCD) remains one of the top causes of high mortality rate. Early prediction of ventricular fibrillation (VF), and hence SCD, can improve the survival chance of a patient by enabling earlier treatment. Heart rate variability analysis (HRV) has been widely adopted by the researchers in VF prediction. Different combinations of features from multiple domains were explored but the spectral analysis was performed without the required preprocessing or on a shorter segment as opposed to the standards of The European and North American Task force on HRV. Thus, our study aimed to develop a robust prediction algorithm by including only time domain and nonlinear features while maintaining the prediction resolution of one minute. Nine time domain features and seven nonlinear features were extracted and classified using support vector machine (SVM) of different kernels. High accuracy of 94.7% and sensitivity of 100% were achieved using extraction of only two HRV features and Gaussian kernel SVM without complicated preprocessing of HRV signals. This algorithm with high accuracy and low computational burden is beneficial for embedded system and real-time application which could help alert the individuals sooner and hence improving patient survival chance