Bio-signal Feature Analysis to Detect Aspiration Caused by Dysphagia.

Dysphagia causes aspiration symptoms and can trigger aspiration pneumonia, poor nutritional status, etc. To address these risks, it is important to properly evaluate dysphagia and link it with treatment and training. However, current dysphagia evaluation methods cannot assess a swallowing function equivalent to that in daily life, owing to the examination method and environment. In this study, we analyzed bio-signal features to realize a system that can detect aspiration symptoms in daily life. Focusing on the neck electrical impedance, swallowing sounds, and a surface electromyogram of the suprahyoid muscles, we created a swallowing-measurement device and analyzed the bio-signals of the throat movement during swallowing. By measuring the swallowing of dysphagic patients, we investigated the characteristic differences, depending on the presence or absence of aspiration symptoms. The analysis results suggest that there were differences in each bio-signal depending on the presence or absence of aspiration symptoms, and these bio-signals could detect aspiration symptoms. This method can detect aspiration symptoms in daily life and evaluate dysphagia more appropriately and simply than current evaluation methods.

Development of a Humanoid Hand System to Support Robotic Urological Surgery.

In robotic urological surgery, it is common for an assistant to use laparoscopic forceps to move organs clear of the surgical working space; however, the assistance efficiency is low. In this study, we aimed to develop a three-fingered humanoid hand with multiple degrees of freedom and a folding mechanism that would allow it to be inserted through a small incision to improve the efficiency of assisting with organs. The bladder (with prostate) and kidneys were selected as the target organs. To achieve stable assistance for these organs, we analyzed three postures: "grasp," "open palm," and "pinch." We verified that the proposed hand can be inserted into the abdominal cavity through a 20 mm port and can assist in a grasping an object of the same size as the target organ with these three movements. Clinical Relevance- The proposed three-fingered, eight-degree-of-freedom humanoid hand with a folding mechanism can be inserted through a 20-mm port and is shown to be able to stably assist in grasping objects of the same size as the kidney and bladder (with prostate) in the urological surgical workspace.

Development of Surgical System Using Bellows-Type Foldable Five-Fingered Robot Hand Inserted through a 20-mm Port.

The purpose of this study was to develop a robotic hand to assist with large organs, instead of using a surgeon, in laparoscopic surgery. Grasping, pinching, and exclusion were performed by three subjects with no medical knowledge. The results indicate that the proposed system can perform surgical operations in hand-assisted laparoscopic surgery when the subjects are proficient. However, the thumb mechanism and the wrist control method were required improvement because it was difficult to approach organs. Surgeons were asked to operate the robotic hand while watching the laparoscope image, and they were able to grasp and pinch with little practice.

Development of Hand-Assistance Device using Hand-Joint Orthosis and Neuromuscular Electrical Stimulation.

The development of self-help devices has attracted attention in the light of improving the activities of daily living (ADL) in patients with finger paralysis. These devices are required to reduce discomfort and enable greater degrees of grasping motions in patients. In this study, we developed a lightweight self-help device that uses neuromuscular electrical stimulation and hand-joint orthosis to finely control the fingers. In addition, we examined the possible grasping actions by testing how well the users of this device exhibited improvements in their ADL. Our results indicate that our self-help device can potentially be adapted to address finger paralysis.

Development of Thin Vibration Sheets Using a Shape Memory Alloy Actuator for the Tactile Feedback of Myoelectric Prosthetic hands.

Because the current myoelectric prosthetic hand does not have a tactile function, the user must always check the condition of the prosthetic hand. Various studies on sensory feedback have been conducted to address this problem, but several devices used in them cannot be integrated with artificial limbs, and wearing the devices is a burden on the user. To solve this problem, we developed thin vibration stimulation sheets using shape memory alloy (SMA) actuators. We then conducted an experiment on the effect of the change in shape at the contact part between the sheet and the skin on perception and confirmed that it would be easier to perceive vibration when the skin was deformed in a wider range. In addition, we investigated the number of distinguishable stimulus intensity levels and identification of stimulus positions. According to the results, the stimulus presented by the developed vibration sheet could be identified in three stages without learning about the stimulus, and the stimulation position by the vibration sheet could be identified with the same or higher accuracy as that of the disk-type vibration motor used in the existing research, although the accuracy decreased when vibrations were presented simultaneously.

Selective motor fascicle transfer and neural-machine interface: case report.

An amputated nerve transferred to a nearby muscle produces a transcutaneously detectable electromyographic signal corresponding to the transferred nerve; this technique is known as targeted muscle reinnervation (TMR). There are 2 issues to overcome to improve this technique: the caliber and the selectivity of the transferred nerve. It is optimal to select and transfer each motor fascicle to achieve highly developed myoelectric arms with multiple degrees-of-freedom motion. The authors report on a case in which they first identified the remnant stumps of the amputated median and radial nerves and then identified the sensory fascicles using somatosensory evoked potentials. Each median nerve fascicle was transferred to the long head branch of the biceps or the brachialis branch, while the short head branch of the biceps was retained for elbow flexion. Each radial nerve fascicle was transferred to the medial or lateral head branch of the triceps, while the long head branch of the triceps was retained for elbow extension. Electrophysiological and functional tests were conducted in the reinnervated muscles. Functional and electrophysiological improvement was noted, with marked improvement in the identification rate for each digit, forearm, and elbow motion after the selective nerve transfers. The authors note that more selective nerve transfers may be required for the development of prostheses with multiple degrees of freedom.

Development of five-finger robotic hand using master-slave control for hand-assisted laparoscopic surgery.

This study aims to develop a robotic hand as a substitute for a surgeon's hand in hand-assisted laparoscopic surgery (HALS). We determined the requirements for the proposed hand from a surgeon's motions in HALS. We identified four basic behaviors: "power grasp," "precision grasp," "open hand for exclusion," and "peace sign for extending peritoneum." The proposed hand had the minimum necessary DOFs for performing these behaviors, five fingers as in a human's hand, a palm that can be folded when a surgeon inserts the hand into the abdomen, and an arm for adjusting the hand's position. We evaluated the proposed hand based on a performance test and a physician's opinions, and we confirmed that it can grasp organs.

Closed-Loop Control of a Neuroprosthetic Hand by Magnetoencephalographic Signals.

OBJECTIVE: A neuroprosthesis using a brain-machine interface (BMI) is a promising therapeutic option for severely paralyzed patients, but the ability to control it may vary among individual patients and needs to be evaluated before any invasive procedure is undertaken. We have developed a neuroprosthetic hand that can be controlled by magnetoencephalographic (MEG) signals to noninvasively evaluate subjects' ability to control a neuroprosthesis. METHOD: Six nonparalyzed subjects performed grasping or opening movements of their right hand while the slow components of the MEG signals (SMFs) were recorded in an open-loop condition. The SMFs were used to train two decoders to infer the timing and types of movement by support vector machine and Gaussian process regression. The SMFs were also used to calculate estimated slow cortical potentials (eSCPs) to identify the origin of motor information. Finally, using the trained decoders, the subjects controlled a neuroprosthetic hand in a closed-loop condition. RESULTS: The SMFs in the open-loop condition revealed movement-related cortical field characteristics and successfully inferred the movement type with an accuracy of 75.0 ± 12.9% (mean ± SD). In particular, the eSCPs in the sensorimotor cortex contralateral to the moved hand varied significantly enough among the movement types to be decoded with an accuracy of 76.5 ± 10.6%, which was significantly higher than the accuracy associated with eSCPs in the ipsilateral sensorimotor cortex (58.1 ± 13.7%; p = 0.0072, paired two-tailed Student's t-test). Moreover, another decoder using SMFs successfully inferred when the accuracy was the greatest. Combining these two decoders allowed the neuroprosthetic hand to be controlled in a closed-loop condition. CONCLUSIONS: Use of real-time MEG signals was shown to successfully control the neuroprosthetic hand. The developed system may be useful for evaluating movement-related slow cortical potentials of severely paralyzed patients to predict the efficacy of invasive BMI.

Structure design for a Two-DoF myoelectric prosthetic hand to realize basic hand functions in ADLs.

Prosthetic hands are desired by those who have lost a hand or both hands not only for decoration but also for the functions to help them with their activities of daily living (ADL). Prosthetic robotic hands that are developed to fully realize the function of a human hand are usually too expensive to be economically available, difficult to operate and maintain, or over heavy for longtime wearing. The aim of this study is therefore to develop a simplified prosthetic hand (sim-PH), which is to be controlled by myoelectric signals from the user, to realize the most important grasp motions in ADL by trading off the cost and performance. This paper reports the structure design of a two-DoF sim-PH with two motors to drive the CM joint of the thumb and the interlocked MP joints of the other four fingers. In order to optimize the structure, the model of the sim-PH was proposed based on which 7 sim-PHs with different structural parameters were manufactured and tested in a pick-and-place experiment. Correspondence analysis of the experimental results clarified the relationship between the hand functions and the shapes of fingers.

Selective Linear-Regression Model for hand posture discrimination and grip force estimation using surface electromyogram signals.

This paper proposes the method of hand posture discrimination and grip force estimation by means of Selective Linear-Regression Model. Generally, myoelectric hands which discriminate hand posture and estimate grip force at the same time result in unsatisfying results because of complication of EMG signals. Therefore, most of myoelectric hands can control either the force or the posture. However, the proposed method is able to discriminate hand posture and to estimate grip force simultaneously while the accuracy results are achieved. In experiments, EMG signals were measured while hand posture and grip force were changing. As a result, it appears that EMG features increase monotonically with grip force. In addition, increasing forms of EMG features are different on each posture. Based on these experimental results, the authors propose the method for both discriminating hand posture and estimating grip force by means of several linear-regression models which utilize the relationship between the grip force and EMG features on each posture. To evaluate the effectiveness of this method, the failure rates of discrimination and the estimation errors of the proposed method were employed. The results indicate that failure rates and estimation errors are improved significantly.

Brain-machine interface to control a prosthetic arm with monkey ECoGs during periodic movements.

Brain-machine interfaces (BMIs) are promising technologies for rehabilitation of upper limb functions in patients with severe paralysis. We previously developed a BMI prosthetic arm for a monkey implanted with electrocorticography (ECoG) electrodes, and trained it in a reaching task. The stability of the BMI prevented incorrect movements due to misclassification of ECoG patterns. As a trade-off for the stability, however, the latency (the time gap between the monkey's actual motion and the prosthetic arm movement) was about 200 ms. Therefore, in this study, we aimed to improve the response time of the BMI prosthetic arm. We focused on the generation of a trigger event by decoding muscle activity in order to predict integrated electromyograms (iEMGs) from the ECoGs. We verified the achievability of our method by conducting a performance test of the proposed method with actual achieved iEMGs instead of predicted iEMGs. Our results confirmed that the proposed method with predicted iEMGs eliminated the time delay. In addition, we found that motor intention is better reflected by muscle activity estimated from brain activity rather than actual muscle activity. Therefore, we propose that using predicted iEMGs to guide prosthetic arm movement results in minimal delay and excellent performance.

[Development of a reflex electrical stimulation device to assist walking].

This paper is a summary of the biofeedback technology for the reflex electrical stimulation device to assist walking. The experiments showed that electrical stimulation resulted in prominent stimulation with less habituation. The research elements were an input-type brain machine interface (BMI), functional magnetic resonance imaging (f-MRI) analysis to detect brain activity, multi-channel electrical stimulation, reflex stimulation for muscle contraction, and an adaptive rehabilitation fitting to the walking gate. The results showed that neuro rehabilitation may be attained by the integration of these research elements.

Evaluation method for the proficiency level of an operating myoelectric hand using EMG signals.

To evaluate the proficiency level of an operating myoelectric hand, we proposed an evaluation index consisting of the accuracy and the reproducibility of electromyography (EMG) signal patterns. Our proposed method is not an absolute evaluation because we use bio-signals, so it is necessary to verify the correlation between the proposed index and performance evaluation to confirm the usefulness of the index. Therefore, we conducted classification tests on eight forearm motions and verified the correlation between the proposed method and the classification rate. There was a strong correlation between the accuracy and the classification rate. In addition, if the accuracy was high, high reproducibility led to an increase in the classification rate. We conclude that the proposed method can evaluate the proficiency level of a myoelectric hand.

Estimation of handgrip force using frequency-band technique during fatiguing muscle contraction.

In this paper, we propose a force estimation model to compute the handgrip force from SEMG signal during fatiguing muscle contraction tasks. The appropriate frequency range was analyzed using various combinations of a wavelet scale, and the highest accuracy was achieved at a range from 242 to 365 Hz. After that, eight healthy individuals performed a series of static (70%, 50%, 30%, and 20% MVC) and dynamic (0-50% MVC) muscle contraction tasks to evaluate the performance of this technique in comparison with that of former method using the Root Mean Square of the SEMG signal. Both methods had comparable results at the beginning of the experiments, before the onset of muscle fatigue. However, differences were clearly observed as the degree of muscle fatigue began to increase toward the endurance time. Under this condition, the estimated handgrip force using the proposed method improved from 17% to 134% for static contraction tasks and 40% for dynamic contraction tasks. This study overcomes the limitation of the former method during fatiguing muscle contraction tasks and, therefore, unlocks the potential of utilizing the SEMG signal as an indirect force estimation method for various applications.

Quantitative estimation of muscle fatigue using surface electromyography during static muscle contraction.

Muscle fatigue is commonly associated with the musculoskeletal disorder problem. Previously, various techniques were proposed to index the muscle fatigue from electromyography signal. However, quantitative measurement is still difficult to achieve. This study aimed at proposing a method to estimate the degree of muscle fatigue quantitatively. A fatigue model was first constructed using handgrip dynamometer by conducting a series of static contraction tasks. Then the degree muscle fatigue can be estimated from electromyography signal with reasonable accuracy. The error of the estimated muscle fatigue was less than 10% MVC and no significant difference was found between the estimated value and the one measured using force sensor. Although the results were promising, there were still some limitations that need to be overcome in future study.

Development of hand rehabilitation system for paralysis patient - universal design using wire-driven mechanism.

We have developed a hand rehabilitation system for patients suffering from paralysis or contracture. It consists of two components: a hand rehabilitation machine, which moves human finger joints with motors, and a data glove, which provides control of the movement of finger joints attached to the rehabilitation machine. The machine is based on the arm structure type of hand rehabilitation machine; a motor indirectly moves a finger joint via a closed four-link mechanism. We employ a wire-driven mechanism and develop a compact design that can control all three joints (i.e., PIP, DIP and MP ) of a finger and that offers a wider range of joint motion than conventional systems. Furthermore, we demonstrate the hand rehabilitation process, finger joints of the left hand attached to the machine are controlled by the finger joints of the right hand wearing the data glove.

Control strategy for a myoelectric hand: measuring acceptable time delay in human intention discrimination.

In order to enhance controllability of a myoelectric hand, we focus on a gap between the time when a human intends to move a myoelectric hand and the time when the hand actually moves (i.e., time delay). Normally, the myoelectric hand users dislike the time delay because it makes them feel uncomfortable. However, the users learn the time delay within some time ranges and, eventually, get feel comfortable to operate the hand. Thus, we assume, if we reveal the acceptable delay time (i.e., the time the users accept the gap with their learning ability), we can provide more time in a human intention discrimination process, and enhance its success rate. Therefore, we developed a mobile myoelectric hand system with an embedded linux computer, and conducted a ball catch experiment: we investigate the acceptable delay time by adding the delay time (i.e., 120[ms], 170[ms], 220[ms], 270[ms], 320[ms]) into the human intention discrimination process. As a result, we confirmed that the max accept delay time was approximately 170 [ms] that achieves 61% success rate.

Motion classification using epidural electrodes for low-invasive brain-machine interface.

Brain-machine interfaces (BMIs) are expected to be used to assist seriously disabled persons' communications and reintegrate their motor functions. One of the difficult problems to realize practical BMI is how to record neural activity clearly and safely. Conventional invasive methods require electrodes inside the dura mater, and noninvasive methods do not involve surgery but have poor signal quality. Thus a low-invasive method of recording is important for safe and practical BMI. In this study, the authors used epidural electrodes placed between the skull and dura mater to record a rat's neural activity for low-invasive BMI. The signals were analyzed using a short-time Fourier transform, and the power spectra were classified into rat motions by a support vector machine. Classification accuracies were up to 96% in two-class discrimination, including that when the rat stopped, walked, and rested. The feasibility of a low-invasive BMI based on an epidural neural recording was shown in this study.

Self-organized clustering approach for motion discrimination using EMG signal.

In order to control a myoelectric hand, it is necessary to discriminate among motions using electromyography (EMG) signals. One of the biggest problems in doing so is that EMG feature patterns of different motions overlap, and a classifier cannot discriminate clearly between them. Therefore, we propose a motion discrimination method to solve this problem. In this method, representative feature patterns are extracted from the EMG signals by using a self-organized clustering method, and user's intended motions are assigned as class labels to these feature patterns on the basis of the joint angles of the hand and fingers. The classifier learns using training data that consists of feature patterns and class labels, and then discriminates motions. In an experiment, we compared the discrimination rates of the proposed and conventional methods. The results indicate that the discrimination rate obtained with the former is 5-30% higher than that obtained with the latter; this result verifies the effectiveness of our method.