Design and development of an ultra-low-cost electro - resistive band based myo activated prosthetic upper limb

In developing countries, many amputees have no access to the prosthesis. This is due to the challenges of the environment they are living in and to the prohibitive costs of available prostheses. To reduce this gap, a new concept design for an extremely low cost but highly functional upper limb prosthesis is presented. This goal is attained using a low-cost embedded platform (Arduino) and a wearable stretch-sensor adapted from Electro resistive bands (ERBs). In the proposed design, a sensor based on ERB is used to detect residual muscle contraction which detects the volumetric shifts of contraction instead of electromyography signals. The signals received via this sensor is then processed via an Arduino micro-controller to drive a single DC servo motor. The DC servo motor is directly geared onto a claw-style two-fingered prosthesis which is printed in-house from PLA plastic using a standard 3-D printer. The amount of closure of the prosthesis is fed-back to the user via a second ERB sensor directly connected to the claw in the form of haptic feedback. To make the design easier to maintain, the gears and mechanical parts are made so simple that can be crafted even from recovered materials. The entire design of prosthesis is presented in this thesis. The overall cost for the proposed prosthesis is estimated to be AUD 29. The proposed design can be easily scaled up to accommodate more complex designs such as having multiple individual fingers or wrist rotation

Real-time EMG based pattern recognition control for hand prostheses : a review on existing methods, challenges and future implementation

Upper limb amputation is a condition that significantly restricts the amputees from performing their daily activities. The myoelectric prosthesis, using signals from residual stump muscles, is aimed at restoring the function of such lost limbs seamlessly. Unfortunately, the acquisition and use of such myosignals are cumbersome and complicated. Furthermore, once acquired, it usually requires heavy computational power to turn it into a user control signal. Its transition to a practical prosthesis solution is still being challenged by various factors particularly those related to the fact that each amputee has different mobility, muscle contraction forces, limb positional variations and electrode placements. Thus, a solution that can adapt or otherwise tailor itself to each individual is required for maximum utility across amputees. Modified machine learning schemes for pattern recognition have the potential to significantly reduce the factors (movement of users and contraction of the muscle) affecting the traditional electromyography (EMG)-pattern recognition methods. Although recent developments of intelligent pattern recognition techniques could discriminate multiple degrees of freedom with high-level accuracy, their efficiency level was less accessible and revealed in real-world (amputee) applications. This review paper examined the suitability of upper limb prosthesis (ULP) inventions in the healthcare sector from their technical control perspective. More focus was given to the review of real-world applications and the use of pattern recognition control on amputees. We first reviewed the overall structure of pattern recognition schemes for myo-control prosthetic systems and then discussed their real-time use on amputee upper limbs. Finally, we concluded the paper with a discussion of the existing challenges and future research recommendations

Seizure Detection: A Low Computational Effective Approach without Classification Methods

Epilepsy is a severe neurological disorder that is usually diagnosed by using an electroencephalogram (EEG). However, EEG signals are complex, nonlinear, and dynamic, thus generating large amounts of data polluted by many artefacts, lowering the signal-to-noise ratio, and hampering expert interpretation. The traditional seizure-detection method of professional review of long-term EEG signals is an expensive, time-consuming, and challenging task. To reduce the complexity and cost of the task, researchers have developed several seizure-detection approaches, primarily focusing on classification systems and spectral feature extraction. While these methods can achieve high/optimal performances, the system may require retraining and following up with the feature extraction for each new patient, thus making it impractical for real-world applications. Herein, we present a straightforward manual/automated detection system based on the simple seizure feature amplification analysis to minimize these practical difficulties. Our algorithm (a simplified version is available as additional material), borrowing from the telecommunication discipline, treats the seizure as the carrier of information and tunes filters to this specific bandwidth, yielding a viable, computationally inexpensive solution. Manual tests gave 93% sensitivity and 96% specificity at a false detection rate of 0.04/h. Automated analyses showed 88% and 97% sensitivity and specificity, respectively. Moreover, our proposed method can accurately detect seizure locations within the brain. In summary, the proposed method has excellent potential, does not require training on new patient data, and can aid in the localization of seizure focus/origin

Towards Ultra Low-Cost Myoactivated Prostheses

In developing countries, due to the high cost involved, amputees have limited access to prosthetic limbs. This constitutes a barrier for this people to live a normal life. To break this barrier, we are developing ultra-low-cost closed-loop myoactivated prostheses that are easy to maintain manufacture and that do not require electrodes in contact with the skin to work effectively. In this paper, we present the implementation for a simple but functional hand prosthesis. Our simple design consists of a low-cost embedded microcontroller (Arduino), a wearable stretch sensor (adapted from electroresistive bands normally used for “insulation of gaskets” against EM fields), to detect residual muscle contraction as direct muscle volumetric shifts and a handful of common, not critical electronic components. The physical prosthesis is a 3D printed claw-style two-fingered hand (PLA plastic) directly geared to an inexpensive servomotor. To make our design easier to maintain, the gears and mechanical parts can be crafted from recovered materials. To implement a closed loop, the amount of closure of prosthesis is fed back to the user via a second stretch sensor directly connected to claw under the form of haptic feedback. Our concept design comprised of all the parts has an overall cost below AUD 30 and can be easily scaled up to more complicated devices suitable for other uses, i.e., multiple individual fingers and wrist rotation

Cost effective electro-resistive band based myo activated prosthetic upper limb for amputees in the developing world

In several developing areas of the world, many amputees have no access to any assistive devices for a normal living. The complexity and huge cost of current prosthetics in the market limit the reach and its applications to these underserved people. The goal of this work is to design a low-cost and efficient myo activated prosthetic upper limb for persons with disabilities who are living in resource-limited areas. The design of the hand is composed of two fingers which are made up of low-cost material and using rapid prototyping methods. The surface EMG signals are captured through wearable electro-resistive bands (ERBs) connected to the amputee's residual arm. Using an Arduino microcontroller, the signals are processed to drive a DC motor for open and close of hand. The design aims to achieve reduced power, lower weight, lower maintenance cost and ease of assembly

Experimental study to improve "Federica" prosthetic hand and its control system

Modern 3D printing technologies and wide availability of microcontroller boards allow to make active prosthetic devices in a simple way. This is the case of “Federica”, a very low-cost, under-actuated, active hand prosthesis. The five fingers of the prosthesis are moved by a single motor through inelastic tendons. The control system of the prosthesis is proportional to muscle contraction: firstly, EMG was used, then mechanical sensors that measure muscle volumetric variation were successfully utilized. This prosthesis proved to be particularly energy efficient and fast; it provided a general grasp function by adapting the exerted forces, thus allowing to easily catch even deformable objects. This study presents further analyses and design improvements of this prosthesis. In particular, a new, extremely simple but effective conditioning system of a force sensor resistor was presented and tested. In addition, the actual three-dimensional kinematics of a single finger was captured by means of high frame rate cameras and then analyzed. The new sensor conditioning system was characterized. It proved to be as effective as the EMG envelope to proportionally control the hand prosthesis motion, and it allowed an easier connection to common microcontroller boards. Kinematic analysis allowed to accurately reconstruct the actual phalanges motion over time

Fully Open-Access Passive Dry Electrodes BIOADC: Open-Electroencephalography (EEG) Re-Invented

The Open-electroencephalography (EEG) framework is a popular platform to enable EEG measurements and general purposes Brain Computer Interface experimentations. However, the current platform is limited by the number of available channels and electrode compatibility. In this paper we present a fully configurable platform with up to 32 EEG channels and compatibility with virtually any kind of passive electrodes including textile, rubber and contactless electrodes. Together with the full hardware details, results and performance on a single volunteer participant (limited to alpha wave elicitation), we present the brain computer interface (BCI)2000 EEG source driver together with source code as well as the compiled (.exe). In addition, all the necessary device firmware, gerbers and bill of materials for the full reproducibility of the presented hardware is included. Furthermore, the end user can vary the dry-electrode reference circuitry, circuit bandwidth as well as sample rate to adapt the device to other generalized biopotential measurements. Although, not implemented in the tested prototype, the Biomedical Analogue to Digital Converter BIOADC naturally supports SPI communication for an additional 32 channels including the gain controller. In the appendix we describe the necessary modification to the presented hardware to enable this function

Electrodeless FSR Linear Envelope Signal for Muscle Contraction Measurement

From the evaluation of electrical activity of muscles to the development of myoelectric prosthetic control/manmachine interfaces, the electromyography (EMG) signal has always been the first choice for both clinicians and engineers. However, due to the many drawbacks of EMG (e.g. skin preparation, electromagnetic interferences, high sample rate, etc.), researchers have strived to find suitable alternatives. We propose as a valid alternative, the dry-contact, low-cost sensor based on a force sensitive resistor (FSR). This sensor applied to the skin through a hard, circular base senses the muscle contraction mechanically and this signal can be actually employed to directly replace the EMG linear envelope (EMGLE) that is typically used as a control signal in prosthetics applications. To reduce the output drift (resistance) caused by FSR edges and to maintain the FSR sensitivity over a wide input force range, its signal conditioning is implemented with a reference voltage strategy (voltage output proportional to force). In this paper, we focus on the validation experiments aimed at finding the best FSR position(s) to replace a single EMG lead. Simultaneous recording of EMG and FSR, using up to three FSRs placed directly over the EMG electrodes, in the middle of the targeted muscle was performed on a small sample of two volunteer subjects. Our results show a high correlation (up to 0.92) between FSR output and EMG linear envelope