This research is intended to create a prototype to generate controllable finger movement of a robotic prosthetic hand using Electromyography (EMG) signals. The instrumentation used in this project includes a Bitalino bio-signal sensor kit, skin electrodes, Arduino Uno microcontroller and a prosthetic hand. The Bitalino’s primary function is to serve as a means to obtain the EMG signal. The Arduino Uno’s function is to implement the control algorithm and actuate the robotic hand to move as intended. Using an EMG signal based counter, the method of control deemed fairly reliable since there was proportional control over the hand but it was based on the duration of the muscle in tension rather than how tense the muscle was. The overall control of the hand was generally responsive to the biological signal.

This project aims to create an electronically powered and controlled knee brace to aid stroke victims with partial paralysis with their leg muscle rehabilitation process. The newly designed assistive bionic joint takes the functionality of the existing assistive knee braces to the next level by incorporating a control algorithm that uses sensor signals gathered from the patient’s leg muscles. Electromyography (EMG) is used for gathering impulse signals from electrodes placed on key muscles as inputs for the device. The action of each major leg muscle is replicated using a set of fluidic muscles that mimic the functionality of the actual leg muscles. A microcontroller is used to interpret sensor data and adjust the contraction length of the muscles, thereby providing the wearer with augmented strength and mobility. Initial testing of a proof-of-concept prototype has led to finite control over muscle contraction length based on sensor data and has a response time of 280ms from full extension to contraction. Further testing of the brace assembly, fluidic muscles and control system is conducted and the results indicate a 600ms response time due to a step input. This personalized, powered brace has many implications for the enrichment of muscle rehabilitation such as higher patient morale, more muscle activity, and shortened recovery times.

There are strong needs for flexible and stretchable devices for the seamless integration with soft and curvilinear human skin or irregular textured cloths. However, the mechanical mismatch between the conventional rigid electronics and the soft human body results in many issues including contact breakage, or skin irritation. Due to the mechanical and electrical versatility of nanoscale forms, various nanomaterials have rapidly established themselves as promising electronic materials, replacing rigid wafer-based electronics in next-generation wearable devices. Here, we introduce a flexible, wearable bioelectronic system using an elastomeric hybrid nanocomposite, composed of zero-dimensional Carbon Black (CB) and one-dimensional Carbon Nanotubes (CNTs) and silver nanowires (AgNWs) in a polydimethylsiloxane (PDMS) matrix. Those materials were chosen due to their good electrical properties and their different length scale providing a continuous connection in the flexible PDMS matrix. To achieve a homogeneous dispersion, these nanomaterials were mechanically mixed in PDMS under shear flow using an overhead mixer. A hybrid nanocomposite membrane with dimensions of 15 mm diameter was then prepared by replica molding process. The electrical properties of the nanocomposite were measured over 5, 10 and 15hrs mixing time to investigate the point of electrical stability of the electrode and the electrical performance during EMG signal measurement. This soft nanocomposite, laminated on the skin, enables highly sensitive recording of electromyograms.

Commercial available knee prostheses are still limited because most of them comprise passive elements that store and deliver energy during the gait cycle, but without providing additional energy. This inability to provide additional energy affects the performance of passive prostheses, which in some cases demands up to 60% of additional metabolic energy to perform a gait cycle. Recent research works have focused on the design of active knee prostheses, including the development and implementation of control strategies such as electromyographic (EMG) signals. However, the results of such implementations reveal that these control strategies are still limited because of the relatively long time response and inaccurate movements. This paper presents the design of a new biomimetic-controlled knee prosthesis for transfemoral amputation. The aim is to contribute to the development of simple and effective active knee prostheses. The proposed prosthesis consists of a polycentric mechanism obtained from the body-guidance kinematics synthesis of a four bar linkage. This synthesis is based on the natural movements of the human knee, taking into account the shortening effect of the leg during the walking process to avoid trips. The prosthetic knee mimics the human motion of the healthy leg by means of an echo-control strategy. An experimental prototype has been implemented and tested on a workbench. The experimental results have demonstrated the usability of the proposed biomimetic active knee prosthesis.

The need to understand the control strategies utilized by humans in their everyday activity requires the measurement of several variables, which pertain to different aspects of the interaction with the environment. This work focuses on validation and uncertainty quantification of a sensor-fusion suite for the acquisition of biometric information that can be used for the estimation of the control strategy in a variety of everyday tasks. Specifically, the acquisition of biomechanical parameters such as those involving estimations of human ankle mechanical impedance is addressed. The sensor fusion suite that we devised consists of a centralized program that can synchronize and integrate the information coming from several transducers. In this work the system combines a set of two computer vision cameras. The cameras utilize a stereovision algorithm that transforms the markers acquired in Cartesian space with a model in the camera space. Furthermore, a biomechanical model of the human is integrated in the system for the reconstruction of the tracked movements in the joint space. The architecture of the systems allows for the integration of transducers beyond those implemented to date, including inertial measurement units, electromyography and pressure sensors. The system was able to capture the dynamics of an inverted pendulum on a cart mimicking human walking on a treadmill there the mechanical parameters of the ankles where estimated simulating a single stand phase.

Neck pain is common among occupations like dentistry and office work because workers in these professions tend to hold their necks in static flexion for extended periods of time. But there are few products available to help alleviate workers’ neck pain while still allowing them to carry out their daily tasks in varying environments. For example, office workers can use products like ergonomic chairs and desks to help promote proper posture, but these arrangements restrict workers to workspaces equipped with their necessary equipment and proper setup. Meanwhile, in the medical field, products like loupes, lights, and ergonomic workbenches are available to enhance visibility and reduce workers’ neck flexion angles. But these products have yet to fully eliminate the problem of neck pain especially in occupations like dentistry where static neck flexion is common. Therefore, the goal of this project is to develop a new neck support technology which alleviates neck pain caused by static neck flexion while still allowing workers full mobility and functionality in their workplace. Our design decreases muscle loading on the neck during neck flexion by reducing the moment on the neck using a device which acts as a “headrest” to support the head. In turn, the device redistributes forces to the upper body while still allowing full range of motion to the user. More specifically, our design applies an opposing force to the user’s forehead during neck flexion via a force generator attached to a headband which is attached to the head. The force generator is anchored to the upper body to permit use in varying environments without the need for special equipment or setups. We confirmed our design decreases muscle loading by building a prototype then performing surface electromyography (EMG) testing which showed not just a statistically significant reduction in neck muscle activity using one-way analysis of variance, but more distinctly a unanimous decrease in neck muscle activity during neck flexion for all seven test subjects with an overall average decrease of 60% among all subjects and 80% for certain subjects. Once we confirmed our design’s effectiveness in reducing neck muscle activity during static neck flexion which implied the ability to reduce neck strain, we improved our prototype’s functionality and aesthetics based on test subject feedback, our own observations, and dentists’ comments. Then, we performed workplace testing on two dentists with one dentist’s work focused mainly on hygiene while the other dentist’s work focused mostly on dental procedures. Overall, both dentists offered helpful feedback from different dental field perspectives for future prototype improvements with regards to comfort and functionality. They also provided promising comments regarding their visions for future device use which included training dental students on proper posture and applications in other occupations like office work and surgery.

The aim of this paper is to design and develop a low-cost prosthetic arm based on surface electromyography (sEMG) signal activities of the biceps muscle during upper-limb movement. Different methods are described in the literature, but many problems are encountered in dealing with the online processing of raw EMG (rEMG) signals, such as signal sampling and memory requirements. In this paper, the enveloped EMG (eEMG) signal is used as a control signal that reduces signal sampling rate and memory requirements. The relationship between elbow motion and the activity level of the biceps muscle is characterized using relevant extracted features (root mean square (RMS)). Validation of the proposed low-cost system is conducted using comparison with a professional biomedical system (Bioback MP150). In addition, the estimated equation of movements of each subject is estimated based on the recorded data. From this equation, the angle of motion is calculated as the control of the movement of the robotic arm. Finally, the system proposed in this paper considers the eEMG signal rather than the rEMG signal and deals with the signal based on a sample of 1 KHz rather than 10 KHz. This system reduces our target cost (reduction in hardware requirements and processing time) with acceptable accuracy. The experimental results illustrate that the eEMG signal has the same features-print as that of the rEMG signal, and the eEMG signal can generate the control signal required to move the prosthetic arm.

Electromyography (EMG) is a method for monitoring the electrical activity of skeletal muscles. The EMG signal is used to diagnose neuromuscular diseases and muscular injuries. EMG can also be utilized as an indicator of user intent for a muscle contraction for a specific motion. This input signal could be used to control powered exoskeleton prostheses. Limbs with impaired motor function tend to have increased disuse that may result in further muscle weakness. Therapy and other physical activities that increase the use of an impaired limb may contribute to some recovery of motor function. A device that helps to perform activities of daily living may increase usage and enhance recovery. The objective of this project is to make developments toward an EMG controlled assistive rehabilitation system that monitors EMG signals of the bicep and triceps muscles, and drives a motor to assist with arm motion. A motor is used to develop torque that would assist rotations of the arm about the elbow. A pair of EMG sensors (one pair near the biceps and the other near the triceps muscle) transmits electrical activity of the arm to a microcontroller (Raspberry Pi, Raspberry Pi Foundation, United Kingdom). For the prototype, the EMG signal is sampled and rectified within a moving time window to determine the root mean squared (VRMS) value. This value is used by the microcontroller to generate a pulse-width modulated (PWM) signal that controls the motor. Sensors for the motor provide information to an algorithm on the microcontroller. The generated PWM signal is based on the V rms values for the EMG signal. Testing and analysis has shown a correlation between the EMG V rms amplitude and muscle generated torque. The EMG controlled assistive rehabilitation system shows promise for assisting motor function for rotations about the elbow. Further algorithmic development is needed to determine the appropriate amount of assistance from the motor for the motor function indicated by user intent.

Activities of daily living include carrying objects using one hand. Carrying a load using one hand can alter the loading on the musculoskeletal system as well as the walking pattern. The objective of this pilot study was to quantify the ground reaction forces, electromyographic (EMG) activity of trunk muscles, and trunk motion during walking. Nine human volunteers with no symptoms of pain were recruited from the student and employee population of an academic institution. Data were recorded from 8 volunteer subjects. Participants were asked to walk at self-selected speed back and forth at their comfortable speed carrying loads varying from 0 to 25 pounds on right hand on a wooden walking platform for 30 steps/cycles. Motion data were recorded from T1, L1, L3, and S1 vertebrae at a sampling frequency of 120 Hz. Range of Motion (ROM), Correlation Dimension (CoD), and Approximate Entropy (ApEn) was computed using custom written MatLab programs. EMG data were recorded from six muscle groups bilaterally (right and left): Erector Spinae, Multifidus, Latissimus Dorsi, Internal Obliques, External Obliques and Rectus Abdominis at a frequency of 1200 Hz. Root mean square EMG values, Mean and Median Frequency of the EMG data were calculated to observe the effect of increasing load on muscle fatigue using custom developed MatLab program. Ground reaction force (GRF) data were collected using a force plate and the associated 1st peak force (Fz1), 2nd Peak force (Fz3) and minimum force (Fz2) between the two peak forces were calculated during gait cycle. The ROM values had a range from 2.6–3.2 deg. for Lumbar lateral bending (LB), 6.7–8.7 deg. for Thoracic LB. Approximate Entropy (ApEn) values ranged from 0.20–0.40 for Lumbar LB motion and 0.30–0.50 for Thoracic LB motion. Correlation Dimension (CoD) values ranged from 1.20–1.40 for lumbar LB and 1.20–1.30 for Thoracic LB. Normalized GRF increased during walking with increased load. Significance difference (P<0.05) were found for vGRF with increase in load. Motion and EMG data did not reveal any significant differences.

A tendon-driven articulated manipulator with disc-cam pulleys is presented for the kinematic modeling and the motion analysis of a human index finger. Using the proposed model as a foundation, the driving forces of the tendons of human finger could be further evaluated and compared with the estimation from the EMG signals. The motivation of using such a tendon-driven articulated manipulator model to emulate the structure and functionality of a human finger is initiated by the similarities between these two systems, where the joint motions of the systems are both activated by the force transmission of tendons either by the base-driven actuators or the contraction of human muscles. However, the distinction between these two systems is that the extension/flexion motion of a human finger is coupled with the abduction/adduction motion due to the anatomical complexity of the expansion hood of a human finger, while the motion of a general articulated manipulator is not coupled. Moreover, since the shapes of the base and head of phalanges are irregular, the joints of a human finger cannot be simply treated as a perfect revolute joint. Hence, for the motion compatibility between the human finger and the articulated manipulator, the functionality of the expansion hood and the tendon system of a human index finger are identified and the joints are treated as revolute joints with non-circular pulleys. Based on the kinematic model and motion simulation of the finger-alike tendon-driven articulated manipulator is accomplished, and the prototyping model of the manipulator is constructed. Motion comparison between the models with the constant and non-constant moment arms is also implemented.

A subsequent study of obstructed voluntary arm movement extended the relative damping concept, and incorporated the influential factors of the mechanical behavior of the neural, muscular and skeletal system in the control and coordination of arm posture and movement. A significant problem of the study is how this information should be used to modify control signals to achieve desired performance. This study used an Equilibrium Point Hypothesis (EPH) model to examine changes of controlling signals for arm movements in the context of adding perturbation/load in the form of forces/torques. The mechanical properties and reflex actions of muscles of the elbow joint were examined. Brief unexpected torque/force pulses of identical magnitude and time duration were introduced at different stages of the movement in a random order by a pre-programmed 3 degree of freedom (DOF) robotic arm (MOOG FCS HapticMaster). Key to this research is the optimization of B and K for each subject based on their HM only experimental data. The results shown in each of sections confirm that those parameters. Along with an EMG determined VT can be used successfully to model the perturbed trials. The results also show that the subjects may maintain the same control parameters (virtual trajectory, stiffness and damping) regardless of added perturbations that cause substantial changes in EMG activity and kinematics.

Artificial limb control is an active area of research, and the control of prosthetic devices using electromyographic (EMG) interfaces is well established. The authors have previously performed a feasibility study which demonstrated that wireless nerve control of a prosthetic device is possible. This is critical for injuries that result in the loss of muscle which prevent EMG control. The purpose of this study is to extend the authors’ current research by providing enhancements to the system designed in the feasibility study. The objective of this current study is to take simulated nerve signals and transform them into corresponding positional motion control realized by a servo motor. The system designed has four functioning blocks: artificial nerve signal generation, wireless transmitter, wireless receiver, and servo motor. The artificial nerve generator used a PIC microcontroller to simulate and apply the signals directly to the wireless transmitter. A one-byte message, that indicated when the appropriate signal characteristics were met, was wirelessly transmitted. After receiving the message, the wireless receiver sent a corresponding pulse-width modulated (PWM) signal to the servo motor for positional control. Various input signal combinations were used to test the system.

Many diseases and conditions can cause reduced motor function in joints throughout the body. This paper identifies such health issues that affect the function of the wrist and hand in particular. After identifying these conditions, a concept three degree of freedom (two DOFs of the wrist plus cylindrical grip) robotic exoskeleton design is presented that is intended to augment a patient’s remaining function and strength while remaining portable and lightweight. With this in mind, the device is intended to allow a patient to use and operate it independently, without the presence of a therapist. The mechanical design of the exoskeleton is described in depth, along with details of potential actuation methods. The main idea for control of the device is to detect small torque values as a patient attempts a movement and in turn predict what the intended outcome would be if the patient were at full strength. After processing this information the robot would assist the patient in facilitating the anticipated movement. This is in contrast to alternate control methods, which rely primarily on electromyography (EMG) to detect signals to muscles that control certain movements. Electromyography can be unreliable because many of the conditions that cause debilitated function also cause an interruption or break in these signals.

Both athletes and soldiers subject their body to extensive prolonged movements at the price of completing their tasks. The purpose of his study is to show that phase space warping (PSW) concept and smooth orthogonal decomposition (SOD) can be used to extract muscle fatigue related information from easily obtainable and noninvasive movement kinematics data. Two experimental setups are considered: load carrying soldiers walking on a treadmill and subjects performing a sawing motion. PSW and SOD based fatigue related trends are compared against local muscle fatigue markers obtained from surface electromyography (sEMG) measurements. In particular, a decrease in the mean power frequencies (MNF) or median power frequencies (MDF) of the sEMGs are used to indicate the onset of muscle fatigue, since a muscle fatigue causes a shift in the power spectrum of sEMG to lower frequencies.

This paper discusses a new model of neuromuscular control of elbow and shoulder joints based on the Equilibrium Point Hypothesis (EPH). The earlier model [1] suggests that the incorporation of relative damping within reflex loops can maintain the dynamic simplicity of the EPH, while being robust over the range of human joint velocities. The model presented here, extends previous work with the use of experimental Electromyography data of 2 muscles to determine the timing parameters of the virtual trajectories and the inclusion of physiological time delays to account for neural transmission and muscle stimulation/activation delays. This model uses delays presented in the literature by other researchers, with a goal of contributing to a resolution of arguments regarding the controversial arguments in the planning sequences. Therefore, this study attempts to demonstrate the possibility for using descending CNS signals to represent relatively simple, monotonic virtual trajectories of the time varying Equilibrium Point for the control of human arm movement. In addition, the study demonstrates that these virtual trajectories were robust enough to control and coordinated movement of elbow and shoulder joints discussed.

Several healthcare products have been developed and marketed in recent times as a result of people’s growing interest in personal health. Unstable shoes have been introduced to revitalize the muscles of the lower limbs and to modify the gait posture while walking. However, healthcare products for people should first be proved functional and safe, as some of those can sometimes result in severe injuries and side effects. Certification is, therefore, necessary in the case of unstable shoes. In this study, the functionality of unstable shoes was analyzed; it was proved that difference in pressure distribution resulting from the shape of the unstable shoes helps strengthen the muscles of the lower limbs. These analyses focused on the activation of the muscles by employing EMG (Electromyography). However, the approach involving EMG cannot carry out measurements on hidden muscles, and the noise involved is a source of potential error; therefore, this study utilizes the simulation software SIMM (Software for Interactive Musculoskeletal Modeling) for this purpose. We performed a biomechanical study using a full-body musculoskeletal model. Using the captured 3D motion data and ground reaction forces data, kinetic data was calculated in order to determine its influence on the adjacent segments. We captured the movements of six volunteers, all males in their twenties. The volunteers wore both unstable and normal shoes during each trial. This study focuses on the activation of muscles of the lower limbs when wearing unstable shoes. We inspected the muscles and analyzed the disparities between unstable and normal shoes. We observed from experimental results that most muscles of the lower limbs were revitalized. Further, we observed an improvement in the gait posture after unstable shoes were used for a period of 12 weeks. This analysis of inner muscles that cannot be examined by direct methods can help consumers make informed choices regarding healthcare products. Such analysis is made possible by simulation programs such as SIMM.

The ability to track and predict the onset of physiologic fatigue using easily measurable variables is of great importance to both civilian and military activities. In this paper, biomechanical gait variables are used to reconstruct fatigue evolution in subjects walking with a 40 kg load on a level treadmill for two hours. Fatigue is reconstructed in two steps: (1) phase space warping based feature vectors are estimated from gait variable time series; and (2) smooth orthogonal decomposition is used to extract fatigue related trends from these features. These results are verified using independently obtained measures of fatigue from breath-by-breath oxygen consumption ( V˙O 2 ) and surface electromyography (EMG) from a set of leg muscles. V˙O 2 based measures for some subjects show no discernable trends. However, for a subject showing monotonically increasing oxygen consumption, the reconstructed dominant fatigue variable closely track V˙O 2 measure reflecting global systemic fatigue. For the muscles showing variation in EMG-based fatigue measures, the reconstructed fatigue variables also closely track these local muscle trends. The results show that kinematic angles, which are easier quantities to measure in the field, can be used to track and predict the onset of fatigue.

This paper reports on a project to measure and control tremor in individuals with multiple sclerosis (MS). It discusses systems for measuring and modelling upper limb movements and intention tremor, together with initial movement measurement and torque modelling data. The system uses microelectromechanical systems (MEMS) sensors to measure movements of the upper limb representative of everyday tasks. Surface electromyogram (EMG) measurements are taken simultaneously to provide gross muscle activity data. A dynamic model is used to simulate the movements, allowing particular sites to be studied in detail. Initial movement data is presented, comparing analysed EMG data and torque estimated by the dynamic model around the elbow joint. Despite the simple analysis, a good fit was obtained. The dynamic model includes a neuromuscular modelling system which will be used in future work to simulate the interactions between measured movements containing intention tremor and the intention behind them, allowing better understanding of the tremor and creating better descriptions of the tremor. Future work will use measured data and modelling to develop methods of attenuating intention tremor, by providing real-time feedback control of tremor reduction devices, without adversely affecting the underlying intended movement.

The kinetic redundancy in human musculoskeletal trunk system allows for the active control of posture during a specific task while satisfying equilibrium and stability requirements. Such control could aim, for example, to minimise forces in active musculature and stresses in passive tissues. It results in a superfluous system of equations the solution of which not yet satisfactorily achieved despite its importance in evaluation and treatment of spinal disorders. Due to shortcomings in existing reduction, optimisation and EMG-driven models, and combination thereof, a novel kinematics-based finite element approach [1,2] is employed that fully accounts for the synergy between passive and active trunk sub-systems. In this study, the kinematics-based model is applied to determine spinal muscle forces and internal ligamentous passive loads at different disc levels in standing postures under gravity loads with and without 200N weights carried in both hands (positioned symmetrically close to the body, either anteriorly or on sides).

A three-dimensional musculoskeltal model of the human knee is being developed to investigate the forces placed on the four major knee ligaments as a result of muscle contraction. This computational knee model includes the femur, tibia, and patella, seven major muscles, four knee ligaments, and the patellar tendon. A constrained forward dynamic simulation is performed by using EMG data to estimate muscle forces. The ligament forces estimated by the model are verified by means of a robotic-cadaver experimental setup in which the knee flexion angle of a cadaver specimen was controlled by a robot. Estimated muscle forces were dynamically applied to the cadaver knee as it was flexed/extended through a specified range of motion. The ligament strain calculated by the model is compared to the strain measured experimentally and to values found in literature to verify the accuracy of the model.