sEMG Sensor Using Polypyrrole-Coated Nonwoven Fabric Sheet for Practical Control of Prosthetic Hand

One of the greatest challenges of using a myoelectric prosthetic hand in daily life is to conveniently measure stable myoelectric signals. This study proposes a novel surface electromyography (sEMG) sensor using polypyrrole-coated nonwoven fabric sheet as electrodes (PPy electrodes) to allow people with disabilities to control prosthetic limbs. The PPy electrodes are sewn on an elastic band to guarantee close contact with the skin and thus reduce the contact electrical impedance between the electrodes and the skin. The sensor is highly customizable to fit the size and the shape of the stump so that people with disabilities can attach the sensor by themselves. The performance of the proposed sensor was investigated experimentally by comparing measurements of Ag/AgCl electrodes with electrolytic gel and the sEMG from the same muscle fibers. The high correlation coefficient (0.87) between the two types of sensors suggests the effectiveness of the proposed sensor. Another experiment of sEMG pattern recognition to control myoelectric prosthetic hands showed that the PPy electrodes are as effective as Ag/AgCl electrodes for measuring sEMG signals for practical myoelectric control. We also investigated the relation between the myoelectric signals\u27 signal-to-noise ratio and the source impedances by simultaneously measuring the source impedances and the myoelectric signals with a switching circuit. The results showed that differences in both the norm and the phase of the source impedance greatly affect the common mode noise in the signal

Motion Control for an Intelligent Walking Support Machine

Walking is a vital exercise for health promotion and fundamental ability necessary for everyday life. Up to now, many robots for walking support or walking rehabilitation of the elderly and the disabled are reported. In this paper, a new omni-directional walking support machine is developed. The machine can realize walking support by following the user's control intention which is detected according to the user's manipulation. However, the motion of the machine is affected by the nonlinear frictions, center-of-gravity (COG) shifts and loads changes caused by users. It is necessary to improve the machine's motion performance to follow the user intention and support the user. Therefore, this paper describes a motion control method based on digital acceleration control to deal with the problem of nonlinear frictions, COG shifts and loads changes. Simulations are executed and the results demonstrate the feasibility and effectiveness of the proposed digital acceleration control method

Improving the Motion Performance for an Intelligent Walking Support Machine by RLS Algorithm

To make the old people and handicapped people move easily by themselves, an omni-directional walking support machine (WSM) has been developed. In our previous study, to improve the motion performance of the WSM, a digital acceleration control method has been developed to deal with the nonlinear friction. However, the design of the digital acceleration controller requires to know the exact plant parameters of the WSM which are variable due to center of gravity (COG) shift and load changes. The change of the plant parameters affects the motion performance of the digital acceleration control system. Therefore, in this paper, a discrete-time system identification method using recursive least squares (RLS) algorithm is proposed to online identify the WSM’s plant parameters for the digital acceleration controller. Simulations are executed and compared with the digital acceleration controller without using RLS algorithm, and the results demonstrate the feasibility and effectiveness of the proposed control method

One-Channel Surface Electromyography Decomposition for Muscle Force Estimation

Estimating muscle force by surface electromyography (sEMG) is a non-invasive and flexible way to diagnose biomechanical diseases and control assistive devices such as prosthetic hands. To estimate muscle force using sEMG, a supervised method is commonly adopted. This requires simultaneous recording of sEMG signals and muscle force measured by additional devices to tune the variables involved. However, recording the muscle force of the lost limb of an amputee is challenging, and the supervised method has limitations in this regard. Although the unsupervised method does not require muscle force recording, it suffers from low accuracy due to a lack of reference data. To achieve accurate and easy estimation of muscle force by the unsupervised method, we propose a decomposition of one-channel sEMG signals into constituent motor unit action potentials (MUAPs) in two steps: (1) learning an orthogonal basis of sEMG signals through reconstruction independent component analysis; (2) extracting spike-like MUAPs from the basis vectors. Nine healthy subjects were recruited to evaluate the accuracy of the proposed approach in estimating muscle force of the biceps brachii. The results demonstrated that the proposed approach based on decomposed MUAPs explains more than 80% of the muscle force variability recorded at an arbitrary force level, while the conventional amplitude-based approach explains only 62.3% of this variability. With the proposed approach, we were also able to achieve grip force control of a prosthetic hand, which is one of the most important clinical applications of the unsupervised method. Experiments on two trans-radial amputees indicated that the proposed approach improves the performance of the prosthetic hand in grasping everyday objects

Effect of Age on the Human Ability to Identify Fragmented Letters through Visual Interpolation(視覚内挿を通じて断片文字を同定する能力に対する年齢の影響)

雑誌掲載版人間は不完全な文字の同定など視覚内挿にたけている。加齢がこの能力にどのような影響を与えるかを調べるため、20〜76歳の健常者56名の断片文字の同定能力を調べた。アルファベットの断片文字を無作為にパソコンの画面上に表示した。断片文字は無作為に完全な文字から削除できる矩形域(約4×8ピクセル)に作製した。断片文字読解スコアは全対象において、ピクセル50%削除での適正同定は80%以上であったが、適正同定スコアはピクセルの70%、80%、90%削除で有意に減少し、これは男女間或いは年齢間で大きな差異はなかった。断片文字同定能力は健康な高齢者では良好に維持されており、認識機能(言語能力)のある側面を評価するのに利用できると考えられ