Development and Assessment of a Movement Disorder Simulator Based on Inertial Data

The detection analysis of neurodegenerative diseases by means of low-cost sensors and suitable classification algorithms is a key part of the widely spreading telemedicine techniques. The choice of suitable sensors and the tuning of analysis algorithms require a large amount of data, which could be derived from a large experimental measurement campaign involving voluntary patients. This process requires a prior approval phase for the processing and the use of sensitive data in order to respect patient privacy and ethical aspects. To obtain clearance from an ethics committee, it is necessary to submit a protocol describing tests and wait for approval, which can take place after a typical period of six months. An alternative consists of structuring, implementing, validating, and adopting a software simulator at most for the initial stage of the research. To this end, the paper proposes the development, validation, and usage of a software simulator able to generate movement disorders-related data, for both healthy and pathological conditions, based on raw inertial measurement data, and give tri-axial acceleration and angular velocity as output. To present a possible operating scenario of the developed software, this work focuses on a specific case study, i.e., the Parkinson’s disease-related tremor, one of the main disorders of the homonym pathology. The full framework is reported, from raw data availability to pathological data generation, along with a common machine learning method implementation to evaluate data suitability to be distinguished and classified. Due to the development of a flexible and easy-to-use simulator, the paper also analyses and discusses the data quality, described with typical measurement features, as a metric to allow accurate classification under a low-performance sensing device. The simulator’s validation results show a correlation coefficient greater than 0.94 for angular velocity and 0.93 regarding acceleration data. Classification performance on Parkinson’s disease tremor was greater than 98% in the best test conditions

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

A Wearable Mechatronic Device for Hand Tremor Monitoring and Suppression: Development and Evaluation

Tremor, one of the most disabling symptoms of Parkinson\u27s disease (PD), significantly affects the quality of life of the individuals who suffer from it. These people live with difficulties with fine motor tasks, such as eating and writing, and suffer from social embarrassment. Traditional medicines are often ineffective, and surgery is highly invasive and risky. The emergence of wearable technology facilitates an externally worn mechatronic tremor suppression device as a potential alternative approach for tremor management. However, no device has been developed for the suppression of finger tremor that has been validated on a human. It has been reported in the literature that tremor can be selectively suppressed by mechanical loading. Therefore, the objectives of this thesis were to develop a wearable tremor suppression device that can suppress tremor at the wrist and the fingers, and to evaluate it on individuals with PD in a pre-clinical trial. To address these objectives, several experiments were performed to quantify hand tremor; an enhanced high-order tremor estimator was developed and evaluated for tremor estimation; and a wearable tremor suppression glove (WTSG) was developed to suppress tremor in the index finger metacarpophalangeal (MCP) joint, the thumb MCP joint, and the wrist. A total of 18 individuals with PD were recruited for characterizing tremor. The frequencies and magnitudes of the linear acceleration, angular velocity, and angular displacement of tremor in the index finger MCP joint, the thumb MCP joint, and the wrist were quantified. The results showed that parkinsonian tremor consists of multiple harmonics, and that the second and third harmonics cannot be ignored. With the knowledge of the tremor characteristics, an enhanced high-order tremor estimator was developed to acquire better tremor estimation accuracy than its lower-order counterpart. In addition, the evaluation of the WTSG was conducted on both a physical tremor simulator and on one individual with PD. The results of the simulation study proved the feasibility of using the WTSG to suppress tremor; and the results of the evaluation on a human subject showed that the WTSG can suppress tremor motion while allowing the user to perform voluntary motions. The WTSG developed as a result of this work has demonstrated the feasibility of managing hand tremor with a mechatronic device, and its validation on a human subject has provided useful insights from the user\u27s perspectives, which facilitate the transition of the WTSG from the lab to the clinic, and eventually to commercial use. Lastly, an evaluation studying the impact of suppressed tremor on unrestricted joints was conducted on 14 individuals with PD. The results showed a significant increase in tremor magnitude in the unrestricted distal joints when the motions of the proximal joints were restricted. The average increase of the tremor magnitude of the index finger MCP joint, the thumb MCP joint, the wrist and the elbow are 54%, 96%, 124%, and 98% for resting tremor, and 50%, 102%, 49%, and 107% for postural tremor, respectively. Such a result provided additional clinical justification for the significance of the development of a wearable mechatronic device for hand tremor management. Although the focus of this thesis is on hand tremor management, the development and evaluation of a full upper-limb tremor suppression device is required as a future step, in order to advance the use of wearable mechatronic devices as one of the valid tremor treatment approaches

Clinical Decision Support Systems with Game-based Environments, Monitoring Symptoms of Parkinson’s Disease with Exergames

Parkinson’s Disease (PD) is a malady caused by progressive neuronal degeneration, deriving in several physical and cognitive symptoms that worsen with time. Like many other chronic diseases, it requires constant monitoring to perform medication and therapeutic adjustments. This is due to the significant variability in PD symptomatology and progress between patients. At the moment, this monitoring requires substantial participation from caregivers and numerous clinic visits. Personal diaries and questionnaires are used as data sources for medication and therapeutic adjustments. The subjectivity in these data sources leads to suboptimal clinical decisions. Therefore, more objective data sources are required to better monitor the progress of individual PD patients. A potential contribution towards more objective monitoring of PD is clinical decision support systems. These systems employ sensors and classification techniques to provide caregivers with objective information for their decision-making. This leads to more objective assessments of patient improvement or deterioration, resulting in better adjusted medication and therapeutic plans. Hereby, the need to encourage patients to actively and regularly provide data for remote monitoring remains a significant challenge. To address this challenge, the goal of this thesis is to combine clinical decision support systems with game-based environments. More specifically, serious games in the form of exergames, active video games that involve physical exercise, shall be used to deliver objective data for PD monitoring and therapy. Exergames increase engagement while combining physical and cognitive tasks. This combination, known as dual-tasking, has been proven to improve rehabilitation outcomes in PD: recent randomized clinical trials on exergame-based rehabilitation in PD show improvements in clinical outcomes that are equal or superior to those of traditional rehabilitation. In this thesis, we present an exergame-based clinical decision support system model to monitor symptoms of PD. This model provides both objective information on PD symptoms and an engaging environment for the patients. The model is elaborated, prototypically implemented and validated in the context of two of the most prominent symptoms of PD: (1) balance and gait, as well as (2) hand tremor and slowness of movement (bradykinesia). While balance and gait affections increase the risk of falling, hand tremors and bradykinesia affect hand dexterity. We employ Wii Balance Boards and Leap Motion sensors, and digitalize aspects of current clinical standards used to assess PD symptoms. In addition, we present two dual-tasking exergames: PDDanceCity for balance and gait, and PDPuzzleTable for tremor and bradykinesia. We evaluate the capability of our system for assessing the risk of falling and the severity of tremor in comparison with clinical standards. We also explore the statistical significance and effect size of the data we collect from PD patients and healthy controls. We demonstrate that the presented approach can predict an increased risk of falling and estimate tremor severity. Also, the target population shows a good acceptance of PDDanceCity and PDPuzzleTable. In summary, our results indicate a clear feasibility to implement this system for PD. Nevertheless, long-term randomized clinical trials are required to evaluate the potential of PDDanceCity and PDPuzzleTable for physical and cognitive rehabilitation effects

Factors of Micromanipulation Accuracy and Learning

Micromanipulation refers to the manipulation under a microscope in order to perform delicate procedures. It is difficult for humans to manipulate objects accurately under a microscope due to tremor and imperfect perception, limiting performance. This project seeks to understand factors affecting accuracy in micromanipulation, and to propose strategies for learning improving accuracy. Psychomotor experiments were conducted using computer-controlled setups to determine how various feedback modalities and learning methods can influence micromanipulation performance. In a first experiment, static and motion accuracy of surgeons, medical students and non-medical students under different magniification levels and grip force settings were compared. A second experiment investigated whether the non-dominant hand placed close to the target can contribute to accurate pointing of the dominant hand. A third experiment tested a training strategy for micromanipulation using unstable dynamics to magnify motion error, a strategy shown to be decreasing deviation in large arm movements. Two virtual reality (VR) modules were then developed to train needle grasping and needle insertion tasks, two primitive tasks in a microsurgery suturing procedure. The modules provided the trainee with a visual display in stereoscopic view and information on their grip, tool position and angles. Using the VR module, a study examining effects of visual cues was conducted to train tool orientation. Results from these studies suggested that it is possible to learn and improve accuracy in micromanipulation using appropriate sensorimotor feedback and training

Real-time haptic modeling and simulation for prosthetic insertion

In this work a surgical simulator is produced which enables a training otologist to conduct a virtual, real-time prosthetic insertion. The simulator provides the Ear, Nose and Throat surgeon with real-time visual and haptic responses during virtual cochlear implantation into a 3D model of the human Scala Tympani (ST). The parametric model is derived from measured data as published in the literature and accounts for human morphological variance, such as differences in cochlear shape, enabling patient-specific pre- operative assessment. Haptic modeling techniques use real physical data and insertion force measurements, to develop a force model which mimics the physical behavior of an implant as it collides with the ST walls during an insertion. Output force profiles are acquired from the insertion studies conducted in the work, to validate the haptic model. The simulator provides the user with real-time, quantitative insertion force information and associated electrode position as user inserts the virtual implant into the ST model. The information provided by this study may also be of use to implant manufacturers for design enhancements as well as for training specialists in optimal force administration, using the simulator. The paper reports on the methods for anatomical modeling and haptic algorithm development, with focus on simulator design, development, optimization and validation. The techniques may be transferrable to other medical applications that involve prosthetic device insertions where user vision is obstructed

Psychomotor Skill Measurement of Video Game Players

Psychomotor skills are a combination of innate abilities as well as skills developed because of repeated actions. Researchers have dedicated many studies to understand the extent to which past videogame play contributes to psychomotor skills and fine motor control dexterity. However, not all gamers are created equal. With today\u27s proliferation of platforms, many people are gamers who never pick up a controller. Grouping all gamers together forms dangerous confounds when trying to generalize across a population as diverse as today\u27s gamers. The current study aims to study a population comprised only of gamers to see if there are significant differences in their psychomotor skills. A psychomotor skills test has been developed, which is designed to simulate proven physical tests, with the express purpose of exposing differences between gamers. After filling out an extensive survey of gaming habits, participants completed the psychomotor skills test. Participants were then grouped by measured psychomotor ability and a selection of high and low performing gamers completed four tutorial exercises on the dV-Trainer by Mimic Technologies, a validated robotic laparoscopic training device. The study shows that the number of hours reported per week using analog controllers is correlated with the psychomotor score as measured by the newly developed simulation. In particular, the Purdue Pegboard and Finger Tapping simulation software is the best discriminator among members of the gamer population

Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 156)

This bibliography lists 170 reports, articles, and other documents introduced into the NASA scientific and technical information system in June 1976

A Sensor Based Approach to Analyzing Motion in Medical Applications: AV Fistula Cannulation and Rett Syndrome

Sensor based motion analysis is employed to assess frequency, severity and duration of Rett syndrome hand stereotypies as well as soft tissue palpation of an arteriovenous fistula. The only prior quantification of Rett symptoms have been visually in a clinical setting; defining palpation skill is largely unprecedented aside from breast tissue examination. We evaluate various sensors used to track motion, measure electromyography, galvanic skin response, and heart rate. The Leap motion controller is evaluated for the viability of tracking hand palpation. Verification tests are performed for determining the feasibility, accuracy, and precision of each sensor. A static phantom was defined as the two endpoints of each of the six fistulas within the palpation simulator and the accuracy of the Leap Motion was \u3c0.9 cm. The 9 DOF motion sensor, EMG sensor, and heart rate sensor all pass their respective verification tests. The galvanic skin response sensor needs further thought into where the electrodes should be placed for proper readings to ensue. All sensors present acceptable precision and accuracy values within the proposed environment; improvements still need to be made for increased performance. Once resolved and perfected, validation studies should verify the preliminary trends of Rett patients’ hand stereotypy quantification and palpation by expert versus novice hemodialysis nurses