Papers by Alexander Astaras
MOCAST Conference Proceedings, 2025
Stroke is estimated to affect about 5,500,000 people annually worldwide, with 60-80% of survivors... more Stroke is estimated to affect about 5,500,000 people annually worldwide, with 60-80% of survivors left with unilateral weakness in their extremities. Other diseases and accidents also contribute to limited mobility and paralysis for millions of patients worldwide. This paper presents the design of a wearable glove that has the capability to curl, extend and resist finger movement by providing forces similar to those produced by agonist and antagonist muscles. The aim of the device is to assist with physical rehabilitation while also providing haptic feedback to the patient. This is achieved by utilizing a pneumatic soft robotic actuation system to provide the curling force (agonistic movement) and a tendon-based pulley system to provide the opposing force (antagonistic movement). This technological choice allows for flexibility as well as gentle, soft feeling of the device, reducing the chances of injury during the rehabilitation process. The design aims to enrich existing physical rehabilitation repetitive motion training routines, utilizing haptic feedback and limiting the range of finger curling utilizing simulated tendons.
A novel electrical muscle stimulation device for neurorehabilitation applications with adaptable parameter optimization using AI algorithms
2023 12th International Conference on Modern Circuits and Systems Technologies (MOCAST)
Sensors
Background: This article presents the system architecture and validation of the NeuroSuitUp body–... more Background: This article presents the system architecture and validation of the NeuroSuitUp body–machine interface (BMI). The platform consists of wearable robotics jacket and gloves in combination with a serious game application for self-paced neurorehabilitation in spinal cord injury and chronic stroke. Methods: The wearable robotics implement a sensor layer, to approximate kinematic chain segment orientation, and an actuation layer. Sensors consist of commercial magnetic, angular rate and gravity (MARG), surface electromyography (sEMG), and flex sensors, while actuation is achieved through electrical muscle stimulation (EMS) and pneumatic actuators. On-board electronics connect to a Robot Operating System environment-based parser/controller and to a Unity-based live avatar representation game. BMI subsystems validation was performed using exercises through a Stereoscopic camera Computer Vision approach for the jacket and through multiple grip activities for the glove. Ten healthy...
Commercial BCI Control and Functional Brain Networks in Spinal Cord Injury: A Proof-of-Concept
2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)
Spinal Cord Injury (SCI), along with disability, results in changes of brain organization and str... more Spinal Cord Injury (SCI), along with disability, results in changes of brain organization and structure. While sensorimotor networks of patients and healthy individuals share similar patterns, unique functional interactions have been identified in SCI networks. Brain-Computer Interfaces (BCIs) have emerged as a promising technology for movement restoration and rehabilitation of SCI patients. We describe an experimental methodology to combine high-resolution electroencephalography (EEG) for investigation of functional connectivity following SCI and non-invasive BCI control of robotic arms. Two BCI-naïve female subjects, a SCI patient and a healthy control subject participated in the proof-of-concept implementation. They were instructed to perform motor imagery (MI) while watching multiple movements of either arms or legs during walking, while under 128-channel EEG recording. They were, subsequently, asked to control two robotic arms (Mercury v2.0) using a commercial class EEG-BCI. They both achieved comparable performance levels of robotic control, 52.5% for the SCI patient and 56.9% for the healthy control. We performed a feasibility analysis of functional networks on the EEG-BCI recordings. Visual MI allows training on multiple imagined movements and shows promise in investigating differences in functional cortical networks associated with different motor tasks. This approach could allow the implementation of functional network-based BCIs in the future for complex movement control.
International Journal of Computing, Aug 1, 2014
Measuring the position of a medical instrument inside the human body can be performed with variou... more Measuring the position of a medical instrument inside the human body can be performed with various methods. One option is to measure the phase shift of the signal originating from a transmitter embedded into the tip of the medical instrument, determining its displacement with respect to a set of stationary receivers. The phase shift is converted into a low frequency voltage with the use of a Phased Locked Loop (PLL). This voltage can subsequently be converted into displacement, providing the position of the medical instrument in one (1D), two (2D) and three (3D) dimensions using trilateration. The instrument's displacement can be defined in either the time or frequency domain. This paper presents a novel method for constant velocity displacement of the transmitter, using either the Locally Weighted Scatter-Plot Smoothing (LOWESS) curve fitting method or a Lomb-Scargle periodogram. The Lomb-Scargle periodogram is based on the least-squares power spectrum and can be used instead of waveform smoothing and measurement into the time domain, providing more precise and accurate measurement results as compared to LOWESS curve fitting method.
Visual Versus Kinesthetic Motor Imagery for BCI Control of Robotic Arms (Mercury 2.0)
2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS), 2017
Motor Imagery (MI), the mental execution of an action, is widely applied as a control modality fo... more Motor Imagery (MI), the mental execution of an action, is widely applied as a control modality for electroencephalography (EEG) based Brain-Computer Interfaces (BCIs). Different approaches to MI have been implemented, namely visual observation (VMI) or kinesthetic rehearsal (KMI) of movements. Although differences in brain activity during VMI or KMI have been studied, no investigation with regards to their suitability for BCI applications has been made. The choice of MI approach could affect individual performance during BCI control, especially for off-the-shelf BCI systems, where ease of use and fast reliable results is the target. Whether for healthy individuals or clinical applications, if such systems are expected to reach consumer maturity, best practices for their use should be investigated. We designed a study to compare VMI and KMI as control modalities of an off-the-shelf EEG-BCI system. 30 healthy individuals (18 male, 12 female) participated in the study, operating two house-developed robotic arms (Mercury 2.0) using an Emotiv EPOC EEG-BCI. They were asked to use first VMI and then KMI to achieve BCI control and we compared the training and success rates. In our study, KMI achieved higher skill percentages during imagery training but VMI achieved higher success rates during BCI control of both robotic arms. Nonetheless, observed differences did not exceed significance thresholds. Individual differences could play a major role in MI performance and should be taken into account when choosing which modality to train for the use of a BCI system.
Perceived psychometric characteristics of the «Mercury» prototype robotic arm for rehabilitation applications
Neurorobotics: review of underlying technologies, current developments, and future directions
Neurotechnology: Methods, advances and applications, 2020
This one-stop reference on neurotechnology (the integration of technology and neuroscience) cover... more This one-stop reference on neurotechnology (the integration of technology and neuroscience) covers everything from tools and methods to advances, applications and future directions. The authors present fast, accurate and reliable tools and methods to help professionals make better decisions, reduce subjective errors, and develop better diagnoses. Topics covered include neuroengineering; neurorehabilitation; neurorobotics, neurophotonics; image analysis and processing for neuroscience, virtual and augmented reality in neuroscience; and much more. Ideal for researchers and practitioners in neurotechnology and allied fields.
Sensors, 2021
Recent advances in the field of neural rehabilitation, facilitated through technological innovati... more Recent advances in the field of neural rehabilitation, facilitated through technological innovation and improved neurophysiological knowledge of impaired motor control, have opened up new research directions. Such advances increase the relevance of existing interventions, as well as allow novel methodologies and technological synergies. New approaches attempt to partially overcome long-term disability caused by spinal cord injury, using either invasive bridging technologies or noninvasive human–machine interfaces. Muscular dystrophies benefit from electromyography and novel sensors that shed light on underlying neuromotor mechanisms in people with Duchenne. Novel wearable robotics devices are being tailored to specific patient populations, such as traumatic brain injury, stroke, and amputated individuals. In addition, developments in robot-assisted rehabilitation may enhance motor learning and generate movement repetitions by decoding the brain activity of patients during therapy. T...
Wireless Communications and Mobile Computing, 2017
Patients suffering from life-changing disability due to Spinal Cord Injury (SCI) increasingly ben... more Patients suffering from life-changing disability due to Spinal Cord Injury (SCI) increasingly benefit from assistive robotics technology. The field of brain-computer interfaces (BCIs) has started to develop mature assistive applications for those patients. Nonetheless, noninvasive BCIs still lack accurate control of external devices along several degrees of freedom (DoFs). Unobtrusiveness, portability, and simplicity should not be sacrificed in favor of complex performance and user acceptance should be a key aim among future technological directions. In our study 10 subjects with SCI (one complete) and 10 healthy controls were recruited. In a single session they operated two anthropomorphic 8-DoF robotic arms via wireless commercial BCI, using kinesthetic motor imagery to perform 32 different upper extremity movements. Training skill and BCI control performance were analyzed with regard to demographics, neurological condition, independence, imagery capacity, psychometric evaluation,...
BioMed Research International, 2017
Advances in neural interfaces have demonstrated remarkable results in the direction of replacing ... more Advances in neural interfaces have demonstrated remarkable results in the direction of replacing and restoring lost sensorimotor function in human patients. Noninvasive brain-computer interfaces (BCIs) are popular due to considerable advantages including simplicity, safety, and low cost, while recent advances aim at improving past technological and neurophysiological limitations. Taking into account the neurophysiological alterations of disabled individuals, investigating brain connectivity features for implementation of BCI control holds special importance. Off-the-shelf BCI systems are based on fast, reproducible detection of mental activity and can be implemented in neurorobotic applications. Moreover, social Human-Robot Interaction (HRI) is increasingly important in rehabilitation robotics development. In this paper, we present our progress and goals towards developing off-the-shelf BCI-controlled anthropomorphic robotic arms for assistive technologies and rehabilitation applica...
2016 5th International Conference on Modern Circuits and Systems Technologies (MOCAST), 2016
Measuring the position of a medical instrument inside the human body can be performed with variou... more Measuring the position of a medical instrument inside the human body can be performed with various methods. One option is to measure the phase shift of the signal originating from a transmitter embedded into the tip of the medical instrument, determining its displacement with respect to a set of stationary receivers. The phase shift is converted into a low frequency voltage with the use of a Phased Locked Loop (PLL). This voltage can subsequently be converted into displacement, providing the position of the medical instrument in one (1D), two (2D) and three (3D) dimensions using trilateration. The instrument's displacement can be defined in either the time or frequency domain. This paper presents a novel method for constant velocity displacement of the transmitter, using either the Locally Weighted Scatter-Plot Smoothing (LOWESS) curve fitting method or a Lomb-Scargle periodogram. The Lomb-Scargle periodogram is based on the least-squares power spectrum and can be used instead of waveform smoothing and measurement into the time domain, providing more precise and accurate measurement results as compared to LOWESS curve fitting method.
Non-intrusive infant monitoring, sensor data fusion and tele-alerting prototype system (asmart cot MAIA2)
Proceedings of the 8th ACM International Conference on PErvasive Technologies Related to Assistive Environments, 2015
Sleep monitoring is an increasingly popular practice, both for medical and lifestyle purposes. In... more Sleep monitoring is an increasingly popular practice, both for medical and lifestyle purposes. In the case of infant safety monitoring, however, most of the devices used are inapplicable due to the utilisation of wires, cords, obtrusive sensors, constant radio wave transmission, low sensitivity and specificity. We proposed and are currently developing the second generation of a portable, unobtrusive infant safety system that can be fitted to most existing cots and can wirelessly tele-alert the infant's carers in case of emergency or other pre-defined circumstances. The MAIA system is based on the real-time algorithmic fusion of data obtained from multiple sensors distributed around the infant's cot, as part of a reasonably priced system which is quick to install, requires no alteration of existing infant care routines and demonstrates a high level of sensitivity and specificity.
2015 International Conference on Advanced Robotics (ICAR), 2015
Joystick-based teleoperation is a dominant method for remotely controlling various types of robot... more Joystick-based teleoperation is a dominant method for remotely controlling various types of robots, such as excavators, cranes, and space telerobotics. Our ultimate goal is to create effective methods for training and assessing human operators of joystick-controlled robots. Towards that goal, an extensive study consisting of a total of 38 experimental subjects on both simulated as well as a physical robot, using either no feedback or auditory feedback, has been performed. In this paper, we present the complete experimental setup and we report only on the 18 experimental subjects teleoperating the simulated robot. Multiple observables were recorded, including not only joystick and robot angles and timings, but also subjective measures of difficulty, personality and usability data, and automated analysis of facial expressions and blink rate of the subjects. Our initial results indicate that: First, that the subjective difficulty of teleoperation with auditory feedback has smaller variance as compared to teleoperation without feedback. Second, that the subjective difficulty of a task is linearly related with the logarithm of task completion time. Third, we introduce two important indicators of operator performance, namely the Average Velocity of Robot Joints (AVRJ), and the Correct-to-Wrong-Joystick Direction Ratio (CWJR), and we show how these relate to accumulated user experience and with task time. We conclude with a forward-looking discussion including future steps.
Handbook of Research on Innovations in the Diagnosis and Treatment of Dementia
This chapter provides a review of pilot studies and clinical trials which have been performed to ... more This chapter provides a review of pilot studies and clinical trials which have been performed to date on serious gaming (exergaming) for the elderly. It is a novel pre-emptive approach to help maintain seniors physically and mentally fit, maximising the time for which they are capable of living independently at their own residence. Several studies are reviewed which show that this is a beneficial arrangement for both the elderly and the national healthcare system. We argue that by using exergaming it may be possible to pre-empt and delay the most common ailments that typically force the elderly to -often reluctantly- leave their residence and seek admission to a nursery home: dementia, arthritis, stroke and cardiovascular disease. Finally, we suggest possible next steps and focal points for pre-emptive exergaming geriatric research and conclude that much larger clinical trials are required to obtain conclusive results about the efficacy of this novel approach.
IEEE journal of biomedical and health informatics, Jan 24, 2014
This paper presents a novel method for tracking the position of a medical instrument's tip. T... more This paper presents a novel method for tracking the position of a medical instrument's tip. The system is based on phase locking a high frequency signal transmitted from the medical instrument's tip to a reference signal. Displacement measurement is established having the loop open, in order to get a low frequency voltage representing the medical instrument's movement; therefore, positioning is established by means of conventional measuring techniques. The Voltage Controlled Oscillator (VCO) stage of the Phase Locked Loop (PLL), combined to an appropriate antenna comprise the associated transmitter located inside the medical instrument tip. All the other low frequency PLL components, Low Noise Amplifier (LNA) and Mixer, are located outside the human body, forming the receiver part of the system. The operating details of the proposed system were coded in Verilog-AMS. Simulation results indicate robust medical instrument tracking in one dimension (1D). Experimental evaluat...
Hippokratia, 2008
Biomedical signal monitoring can counteract the risk of human operator error due to inattention o... more Biomedical signal monitoring can counteract the risk of human operator error due to inattention or fatigue in safetycritical and restrictive environments, such as in aviation, space, automobile and heavy industrial machinery operation. Real-time biomedical data acquisition is changing through advances in microelectronics fabrication, bio-MEMS and power micro-generators. Such data acquisition and processing systems are becoming increasingly miniaturised, flexible and pervasive, while data is being collected from inside the human body as well as around it. In this paper we review two related research projects exploiting this technological convergence, discuss its implications and suggest future innovation prospects through further similar cross-disciplinary synergies.
Micro Total Analysis Systems 2002, 2002
A state-of-the-art electronic "pill" has been developed for in situ studies of the gastrointestin... more A state-of-the-art electronic "pill" has been developed for in situ studies of the gastrointestinal (GI) tract using integrated circuit and system level integration technologies. The measurement parameters include real time analysis of temperature, pH, conductivity and dissolved oxygen.
This chapter serves as a concise introduction to the subject material, the motivation and aims of... more This chapter serves as a concise introduction to the subject material, the motivation and aims of this project. This thesis aims to examine the hypothesis that stochastic artificial neural network algorithms can be effectively implemented in analogue VLSI hardware using pulsestream design methods. As this proposition has not been investigated before, it will be studied in the context of a small Helmholtz Machine, a relatively simple and wellunderstood binary stochastic neural network architecture, which can be trained by the low-complexity Wake-Sleep unsupervised algorithm. The performance of the prototype hardware developed for the Helmholtz Machine
XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016, 2016
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Papers by Alexander Astaras