Product Design And Development Using Virtual Reality And CAD/CAM System

The complex product evaluation process can be costly and time-consuming throughout various stages of a design process. Therefore, the present project aims to develop a product towards I4.0 by integrating VR technologies and CAD/CAM systems into the PDD process to make possible improvements against the limitations while reducing the time and costs in the development cycle, ensuring its quality and usability. Throughout the screening and scoring processes, a wrist rehabilitation device is selected as a potential product for further development using VR technology. In expectation, the device has a lower production cost and a simpler working mechanism for fulfilling the requirements of the patients. Hence, the spring feature in the proposed concept AC is replaced with a resistance band for providing various semiautomatic wrist motions in 3-DOF. On top of that, a 3D CAD model of the device is created using SOLIDWORKS 2021 and the visualization of its assembly model and animation in a VR environment is carried out with the EpicCADVR application using Oculus Quest 2 VR device. Thus, the continuous PDD process can be much simpler as it allows the users to examine and verify the initial design of parts virtually for further necessary modification. Also, the demonstration of device usage in FPV instructs the wrist mechanism and encourages the patients to move their wrists through an appropriate ROM, emulating the movement therapy. Eventually, the final device product has been constructed by combining the 3D-printed parts with some standard parts acquired, possessing an approximate weight of 600.66 g. Testing of the product has proven the operating performance of 3-DOF wrist motions, such as flexion/extension, pronation/supination, and abduction/adduction. From the analysis, the achieved ROM exceeds the minimum ROM required to perform ADL. Finally, the total estimated production cost of the final product is RM 121.41, which is comparatively lower than other benchmarking products. The device developed in this study demonstrates the potential for utilizing VR technology in the PDD phase with its immersive experience in FPV. Thus, further implementation of VR systems in PDD has been recommended to explore its possibilities against the limitations. Work undertaken in this project represents a significant step towards the realization of the final product with the aid of a VR system

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

Development of Finger ContracturePrevention System For Early Post StrokeRehabilitation

芝浦尾工業大学2016年

Design, implementation, control, and user evaluations of assiston-arm self-aligning upper-extremity exoskeleton

Physical rehabilitation therapy is indispensable for treating neurological disabilities. The use of robotic devices for rehabilitation holds high promise, since these devices can bear the physical burden of rehabilitation exercises during intense therapy sessions, while therapists are employed as decision makers. Robot-assisted rehabilitation devices are advantageous as they can be applied to patients with all levels of impairment, allow for easy tuning of the duration and intensity of therapies and enable customized, interactive treatment protocols. Moreover, since robotic devices are particularly good at repetitive tasks, rehabilitation robots can decrease the physical burden on therapists and enable a single therapist to supervise multiple patients simultaneously; hence, help to lower cost of therapies. While the intensity and quality of manually delivered therapies depend on the skill and fatigue level of therapists, high-intensity robotic therapies can always be delivered with high accuracy. Thanks to their integrated sensors, robotic devices can gather measurements throughout therapies, enable quantitative tracking of patient progress and development of evidence-based personalized rehabilitation programs. In this dissertation, we present the design, control, characterization and user evaluations of AssistOn-Arm, a powered, self-aligning exoskeleton for robotassisted upper-extremity rehabilitation. AssistOn-Arm is designed as a passive back-driveable impedance-type robot such that patients/therapists can move the device transparently, without much interference of the device dynamics on natural movements. Thanks to its novel kinematics and mechanically transparent design, AssistOn-Arm can passively self-align its joint axes to provide an ideal match between human joint axes and the exoskeleton axes, guaranteeing ergonomic movements and comfort throughout physical therapies. The self-aligning property of AssistOn-Arm not only increases the usable range of motion for robot-assisted upper-extremity exercises to cover almost the whole human arm workspace, but also enables the delivery of glenohumeral mobilization (scapular elevation/depression and protraction/retraction) and scapular stabilization exercises, extending the type of therapies that can be administered using upper-extremity exoskeletons. Furthermore, the self-alignment property of AssistOn-Arm signi cantly shortens the setup time required to attach a patient to the exoskeleton. As an impedance-type device with high passive back-driveability, AssistOn- Arm can be force controlled without the need of force sensors; hence, high delity interaction control performance can be achieved with open-loop impedance control. This control architecture not only simpli es implementation, but also enhances safety (coupled stability robustness), since open-loop force control does not su er from the fundamental bandwidth and stability limitations of force-feedback. Experimental characterizations and user studies with healthy volunteers con- rm the transparency, range of motion, and control performance of AssistOn- Ar

A virtual hand assessment system for efficient outcome measures of hand rehabilitation

Previously held under moratorium from 1st December 2016 until 1st December 2021.Hand rehabilitation is an extremely complex and critical process in the medical rehabilitation field. This is mainly due to the high articulation of the hand functionality. Recent research has focused on employing new technologies, such as robotics and system control, in order to improve the precision and efficiency of the standard clinical methods used in hand rehabilitation. However, the designs of these devices were either oriented toward a particular hand injury or heavily dependent on subjective assessment techniques to evaluate the progress. These limitations reduce the efficiency of the hand rehabilitation devices by providing less effective results for restoring the lost functionalities of the dysfunctional hands. In this project, a novel technological solution and efficient hand assessment system is produced that can objectively measure the restoration outcome and, dynamically, evaluate its performance. The proposed system uses a data glove sensorial device to measure the multiple ranges of motion for the hand joints, and a Virtual Reality system to return an illustrative and safe visual assistance environment that can self-adjust with the subject’s performance. The system application implements an original finger performance measurement method for analysing the various hand functionalities. This is achieved by extracting the multiple features of the hand digits’ motions; such as speed, consistency of finger movements and stability during the hold positions. Furthermore, an advanced data glove calibration method was developed and implemented in order to accurately manipulate the virtual hand model and calculate the hand kinematic movements in compliance with the biomechanical structure of the hand. The experimental studies were performed on a controlled group of 10 healthy subjects (25 to 42 years age). The results showed intra-subject reliability between the trials (average of crosscorrelation ρ = 0.7), inter-subject repeatability across the subject’s performance (p > 0.01 for the session with real objects and with few departures in some of the virtual reality sessions). In addition, the finger performance values were found to be very efficient in detecting the multiple elements of the fingers’ performance including the load effect on the forearm. Moreover, the electromyography measurements, in the virtual reality sessions, showed high sensitivity in detecting the tremor effect (the mean power frequency difference on the right Vextensor digitorum muscle is 176 Hz). Also, the finger performance values for the virtual reality sessions have the same average distance as the real life sessions (RSQ =0.07). The system, besides offering an efficient and quantitative evaluation of hand performance, it was proven compatible with different hand rehabilitation techniques where it can outline the primarily affected parts in the hand dysfunction. It also can be easily adjusted to comply with the subject’s specifications and clinical hand assessment procedures to autonomously detect the classification task events and analyse them with high reliability. The developed system is also adaptable with different disciplines’ involvements, other than the hand rehabilitation, such as ergonomic studies, hand robot control, brain-computer interface and various fields involving hand control.Hand rehabilitation is an extremely complex and critical process in the medical rehabilitation field. This is mainly due to the high articulation of the hand functionality. Recent research has focused on employing new technologies, such as robotics and system control, in order to improve the precision and efficiency of the standard clinical methods used in hand rehabilitation. However, the designs of these devices were either oriented toward a particular hand injury or heavily dependent on subjective assessment techniques to evaluate the progress. These limitations reduce the efficiency of the hand rehabilitation devices by providing less effective results for restoring the lost functionalities of the dysfunctional hands. In this project, a novel technological solution and efficient hand assessment system is produced that can objectively measure the restoration outcome and, dynamically, evaluate its performance. The proposed system uses a data glove sensorial device to measure the multiple ranges of motion for the hand joints, and a Virtual Reality system to return an illustrative and safe visual assistance environment that can self-adjust with the subject’s performance. The system application implements an original finger performance measurement method for analysing the various hand functionalities. This is achieved by extracting the multiple features of the hand digits’ motions; such as speed, consistency of finger movements and stability during the hold positions. Furthermore, an advanced data glove calibration method was developed and implemented in order to accurately manipulate the virtual hand model and calculate the hand kinematic movements in compliance with the biomechanical structure of the hand. The experimental studies were performed on a controlled group of 10 healthy subjects (25 to 42 years age). The results showed intra-subject reliability between the trials (average of crosscorrelation ρ = 0.7), inter-subject repeatability across the subject’s performance (p > 0.01 for the session with real objects and with few departures in some of the virtual reality sessions). In addition, the finger performance values were found to be very efficient in detecting the multiple elements of the fingers’ performance including the load effect on the forearm. Moreover, the electromyography measurements, in the virtual reality sessions, showed high sensitivity in detecting the tremor effect (the mean power frequency difference on the right Vextensor digitorum muscle is 176 Hz). Also, the finger performance values for the virtual reality sessions have the same average distance as the real life sessions (RSQ =0.07). The system, besides offering an efficient and quantitative evaluation of hand performance, it was proven compatible with different hand rehabilitation techniques where it can outline the primarily affected parts in the hand dysfunction. It also can be easily adjusted to comply with the subject’s specifications and clinical hand assessment procedures to autonomously detect the classification task events and analyse them with high reliability. The developed system is also adaptable with different disciplines’ involvements, other than the hand rehabilitation, such as ergonomic studies, hand robot control, brain-computer interface and various fields involving hand control

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

Complimentary Care: Opportunity to Explore Non-Drug Pain Management

Aristotle (4th century B.C.), he said pain as emotion, being the opposite of pleasure. But Buddha says “Pain is the outcome of sin”, as proof that an independently was possessed by demons. In some religions it is the cost of attachment. Medical management thus may be less of a preference than Spiritual counseling. Many non-physiologic factors (psychological, familial and societal attitudes, life stressors, and cultural or spiritual) contributing to the experience of and response to pain. Emotional stress, like, anxiety and depression known a key play in understanding of pain. Endless hurt is related with expanded dimensions of burdensome side effects, anxiety, and insomnia paying little heed to disability status. It has both modifiable factors (mental health, co-morbidities, smoking, alcohol, obesity, physical activity/exercise, sleep, nutrition, economic status and occupational) and non-modifiable factors (age, sex, cultural and socioeconomic background, history of trauma/ injury/ interpersonal violence, heritage). Chronic pain affects 20% of the European population and is commoner in women, older people, and with relative deprivation