Muhammad Saad bin Imtiaz

Bio: Muhammad Saad bin Imtiaz is an academic researcher from Air University (United States Air Force). The author has contributed to research in topics: Exoskeleton. The author has an hindex of 1, co-authored 1 publications receiving 1 citations.

Design of Portable Exoskeleton Forearm for Rehabilitation of Monoparesis Patients Using Tendon Flexion Sensing Mechanism for Health Care Applications

Abstract: Technology plays a vital role in patient rehabilitation, improving the quality of life of an individual. The increase in functional independence of disabled individuals requires adaptive and commercially available solutions. The use of sensor-based technology helps patients and therapeutic practices beyond traditional therapy. Adapting skeletal tracking technology could automate exercise tracking, records, and feedback for patient motivation and clinical treatment interventions and planning. In this paper, an exoskeleton was designed and subsequently developed for patients who are suffering from monoparesis in the upper extremities. The exoskeleton was developed according to the dimensions of a patient using a 3D scanner, and then fabricated with a 3D printer; the mechanism for the movement of the hand is a tendon flexion mechanism with servo motor actuators controlled by an ATMega2560 microcontroller. The exoskeleton was used for force augmentation of the patient’s hand by taking the input from the hand via flex sensors, and assisted the patient in closing, opening, grasping, and picking up objects, and it was also able to perform certain exercises for the rehabilitation of the patient. The exoskeleton is portable, reliable, durable, intuitive, and easy to install and use at any time.

Sensors and Actuation Technologies in Exoskeletons: A Review

Abstract: Exoskeletons are robots that closely interact with humans and that are increasingly used for different purposes, such as rehabilitation, assistance in the activities of daily living (ADLs), performance augmentation or as haptic devices. In the last few decades, the research activity on these robots has grown exponentially, and sensors and actuation technologies are two fundamental research themes for their development. In this review, an in-depth study of the works related to exoskeletons and specifically to these two main aspects is carried out. A preliminary phase investigates the temporal distribution of scientific publications to capture the interest in studying and developing novel ideas, methods or solutions for exoskeleton design, actuation and sensors. The distribution of the works is also analyzed with respect to the device purpose, body part to which the device is dedicated, operation mode and design methods. Subsequently, actuation and sensing solutions for the exoskeletons described by the studies in literature are analyzed in detail, highlighting the main trends in their development and spread. The results are presented with a schematic approach, and cross analyses among taxonomies are also proposed to emphasize emerging peculiarities.

Infrared Sensing Based Non-Invasive Initial Diagnosis of Chronic Liver Disease Using Ensemble Learning

Abstract: The liver is a vital human body organ and its functionality can be degraded by several diseases such as hepatitis, fatty liver disease, and liver cancer and so forth. Hence, the early diagnosis of liver diseases is extremely crucial for saving human lives. With the rapid development of multimedia technology, it is now possible to design and implement a non-invasive system that can chronic liver diseases. For this purpose, machine learning and Artificial Intelligence (AI) have been used within the past few years. In this regard, digital image processing supported by AI methods has been implemented in the diagnosis of diseases that also showed high reliability. Therefore, in this paper, an iris feature-based non-invasive technique is proposed by incorporating a novel machine-learning algorithm. The experimental setup involved data set for the models’ training included 879 subjects from Pakistan, of which 453 subjects have chronic liver disease and 426 are healthy. The iris images were collected using an infrared camera that consists of a lens, a thermal sensor and digital electronics processing. The lens focuses on the infrared energy on the sensor, using distinctive forms of features twenty-two physiological and thirty-three iris features. The designed classification model for a non-invasive system combined eleven different classifiers and used cross-validation techniques for comparing the results. The overall performance of the model was analyzed using five parameters: accuracy, precision, F-score, specificity, and sensitivity. The results confirmed that the proposed non-invasive model is capable of predicting chronic liver diseases with 98% of accuracy.

A systematic review of technological advancements in signal sensing, actuation, control and training methods in robotic exoskeletons for rehabilitation

Abstract: Purpose The purpose of this review paper is to address the substantial challenges of the outdated exoskeletons used for rehabilitation and further study the current advancements in this field. The shortcomings and technological developments in sensing the input signals to enable the desired motions, actuation, control and training methods are explained for further improvements in exoskeleton research. Design/methodology/approach Search platforms such as Web of Science, IEEE, Scopus and PubMed were used to collect the literature. The total number of recent articles referred to in this review paper with relevant keywords is filtered to 143. Findings Exoskeletons are getting smarter often with the integration of various modern tools to enhance the effectiveness of rehabilitation. The recent applications of bio signal sensing for rehabilitation to perform user-desired actions promote the development of independent exoskeleton systems. The modern concepts of artificial intelligence and machine learning enable the implementation of brain–computer interfacing (BCI) and hybrid BCIs in exoskeletons. Likewise, novel actuation techniques are necessary to overcome the significant challenges seen in conventional exoskeletons, such as the high-power requirements, poor back drivability, bulkiness and low energy efficiency. Implementation of suitable controller algorithms facilitates the instantaneous correction of actuation signals for all joints to obtain the desired motion. Furthermore, applying the traditional rehabilitation training methods is monotonous and exhausting for the user and the trainer. The incorporation of games, virtual reality (VR) and augmented reality (AR) technologies in exoskeletons has made rehabilitation training far more effective in recent times. The combination of electroencephalogram and electromyography-based hybrid BCI is desirable for signal sensing and controlling the exoskeletons based on user intentions. The challenges faced with actuation can be resolved by developing advanced power sources with minimal size and weight, easy portability, lower cost and good energy storage capacity. Implementation of novel smart materials enables a colossal scope for actuation in future exoskeleton developments. Improved versions of sliding mode control reported in the literature are suitable for robust control of nonlinear exoskeleton models. Optimizing the controller parameters with the help of evolutionary algorithms is also an effective method for exoskeleton control. The experiments using VR/AR and games for rehabilitation training yielded promising results as the performance of patients improved substantially. Research limitations/implications Robotic exoskeleton-based rehabilitation will help to reduce the fatigue of physiotherapists. Repeated and intention-based exercise will improve the recovery of the affected part at a faster pace. Improved rehabilitation training methods like VR/AR-based technologies help in motivating the subject. Originality/value The paper describes the recent methods for signal sensing, actuation, control and rehabilitation training approaches used in developing exoskeletons. All these areas are key elements in an exoskeleton where the review papers are published very limitedly. Therefore, this paper will stand as a guide for the researchers working in this domain.

Application of additive manufacturing/3D printing on rehabilitation and healthcare: a systematic and citation network analysis review

Abstract: AbstractAbstractApplication of additive manufacturing (AM)/three-dimensional (3D) printing has been a very hot area of research in recent years. The fast and easy way to get the desired product and additive function, such as anti-bacterial materials, embedded nano detective sensors. This study reviewed 197 articles on application of AM/3D printing in rehabilitation and healthcare. The distribution of these articles was investigated in terms of journal type, year of publication, methodologies, and research contexts, etc. Based on the use of citation network analysis (CNA), the result shows the selected articles (197 articles) can be divided into two directions, healthcare and rehabilitation, and healthcare monitoring and materials for 3D printing purposes. All of them represent possible opportunities for future research. Further investigation was done by conducting a Main Path Analysis of the articles to draw a map of knowledge structure within each research domain. Finally, future research opportunities and directions are proposed for each research domain, rehabilitation and healthcare. In addition, use of 3D printing materials with antimicrobial properties for healthcare and rehabilitation is having more potential in future research.Keywords: Densityprosthesishealthcarerehabilitationadditive material Disclosure statementThe authors report there are no competing interests to declare.Additional informationFundingAuthors would like to thank the financial support from The Hong Kong Polytechnic University (account code: ZDCC).