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We propose a walking-assist scheme that exploits residual functions of a hemiplegic patient with a unique case of leg paralysis – involving the somatic reflex that prevented her from raising her left leg, leaving it to drag on the ground instead. She prevented this by pressing a specific spot on her leg with her fingers. We measured electromyographic (EMG) signals and found that although she could not voluntary activate her thigh and tibial muscle, mechanical stimulus activates them. We concluded that her paralysis was a unique somatic reflex of her nerve system and we developed a mechanical stimulator that she used to train her muscles in walking and improved her gait to approach that of an unparalyzed person.

In this paper, we introduce an educational robotics approach featuring a unique robotic development kit - hardware, software, and instructions - that concretely encourages student interest in and curiosity about science and technology. The kit was developed based on practical policies: easy construction, low cost, creative activity, and enjoyable education. It uses common materials such as plastic bottles, RC servomotors, and hot glue, and provides three different controllers with instructions - a sensor-motor controller, an electromyography (EMG) interface controller, and a teaching-playback controller. The kit thus offers more custom access to both robot structure and control architecture than similar kits and encourages students to become engaged creatively. The three robotics courses for undergraduates and graduates we have conducted thus far to provide an understanding of robotics and embodied artificial intelligence (AI) have confirmed that some of locomotive robots explicitly exploit their own dynamics - also known as “morph-functionality” - an embodied AI concept. An evaluation of this approach for course hours, task achievement, student interest, and the influence of assistance confirmed conclusively that students experienced creativity in such robotics courses.

To realize a low-invasive and high accuracy BMI (Brain-machine interface) system, we have already developed a fully-implantable wireless BMI system which consists of ECoG neural electrode arrays, neural recording ASICs, a Wi-Fi based wireless data transmitter and a wireless power receiver with a rechargeable battery. For accurate estimation of movement intentions, it is important for a BMI system to have a large number of recording channels. In this paper, we report a new multi-channel BMI system which is able to record up to 4096-ch ECoG data by multiple connections of 64-ch ASICs and time division multiplexing of recorded data. This system has an ultra-wide-band (UWB) wireless unit for transmitting the recorded neural signals to outside the body. By preliminary experiments with a human body equivalent liquid phantom, we confirmed 4096-ch UWB wireless data transmission at 128 Mbps mode below 20 mm distance.

This paper aims at estimating a joint angle from EMG signals. A bilinear model reflecting a muscle viscous-elastic property changing with its activity is introduced as the dynamic equation. Learning parameters of the dynamical model will decrease the effect from the difference of human users. The signal separation techniques based on the principal or independent component analysis are adopted to solve a problem about noises and crosstalk in EMG measurements, by applying them to some signals detected from some numbers of electrodes attached around the body parts. Experiments on the hand dorsal extension/palmar flexion evaluates some estimation results of angle deviation in the actual hand movements.

In this paper we investigated the morphology and controller of biped robots. We viewed them as design components that together can induce dynamically stable bipedal locomotion. We conducted coupled evolution of the morphology and controller of a biped robot, consisting of nine links and eight joints, actuated by oscillators without sensor feedback in three-dimensional simulation. As a result, both pseudo-passive dynamic walkers and active-control walkers emerged, but the pseudo-passive dynamic walkers showed more dynamic stability than the active-control walkers. This is because compliant components in morphology function as noise filters and passive oscillators. Analysis on this latter class of walkers revealed that this was achieved by two novel functions: self-stabilization and self-regulation. Because these functions were handled by the passive dynamics induced in the robot morphology, due to its compliance, we concluded that a computational trade-off between the controller and morphology occurs in these devices. Finally, we have concluded that appropriate compliance is a key to achieving dynamical stability during locomotion.

In order to realize a low-invasive and high accuracy Brain-Machine Interface (BMI) system for clinical applications, a super multi-channel recording system was developed in which 4096 channels of Electrocorticogram (ECoG) signal can be amplified and transmitted to outside the body by using an Ultra Wide Band (UWB) wireless system. Also, a high density, flexible electrode array made by using a Parylene-C substrate was developed that is composed of units of 32-ch recording arrays. We have succeeded in an evaluation test of UWB wireless transmitting using a body phantom system.

Non-invasive localization of certain brain functions may be mapped on a millimeter level. However, the inter-electrode spacing of common clinical brain surface electrodes still remains around 10 mm, and some electrodes fail to measure cortical activity due to unconformable plain electrode sheets. Here, we present details on development of implantable electrodes for attaining higher quality electrocorticographic signals for use in functional brain mapping and brain-machine interfaces. We produced personalized sheet electrodes after the creation of individualized molds using a 3D-printer. We created arrays to fit the surface curvature of the brain and inside the central sulcus, with inter-electrode distances of 2.5 mm. Rat experiments undertaken indicated no long term toxicity. We were also able to custom design, rapidly manufacture, safely implant and confirm the efficacy of personalized electrodes, including the capability to attain meaningful high gamma-band information in an amyotrophic lateral sclerosis patient. This sheet electrode may contribute to the higher performance of BMI's.

This paper presents a wireless data transmission technology using ultra wideband (UWB) signals in order to realize ultra multi-channel neural recoding system for wireless brain-machine interface (BMI). In order to show feasibility of the use of UWB signals in the application, a testbed has been developed, which consists of a liquid phantom and UWB radio system. The liquid phantom has been developed as to provide electrical constants equivalent to tissues of human body. The UWB radio system which occupies bandwidth of 1800 MHz to send 133 × 10 6 pulses per second. A small-size UWB has been employed, which was fabricated for this application. As a result of experiment with the testbed, it has been demonstrated that bit error ratio (BER) at distance of 20 mm between an implanted device and on-body antenna becomes less than 10 -4 .

Brain Machine Interface (BMI) is a system that assumes user's intention by analyzing user's brain activities and control devices with the assumed intention. It is considered as one of prospective tools to enhance paralyzed patients' quality of life. In our group, we especially focus on ECoG (electro-corti-gram)-BMI, which requires surgery to place electrodes on the cortex. We try to implant all the devices within the patient's head and abdomen and to transmit the data and power wirelessly. Our device consists of 5 parts: (1) High-density multi-electrodes with a 3D shaped sheet fitting to the individual brain surface to effectively record the ECoG signals; (2) A small circuit board with two integrated circuit chips functioning 128 [ch] analogue amplifiers and A/D converters for ECoG signals; (3) A Wifi data communication & control circuit with the target PC; (4) A non-contact power supply transmitting electrical power minimum 400[mW] to the device 20[mm] away. We developed those devices, integrated them, and, investigated the performance.

The brain-machine interface (BMI) enables us to control machines and to communicate with others, not with the use of input devices, but through the direct use of brain signals. This chapter describes the integrative approach the authors used to develop a BMI system with brain surface electrodes for real-time robotic arm control in severely disabled people, such as amyotrophic lateral sclerosis patients. This integrative BMI approach includes effective brain signal recording, accurate neural decoding, robust robotic control, a wireless and fully implantable device, and a noninvasive evaluation of surgical indications.

A brain machine interface (BMI) provides the possibility of controlling such external devices as prosthetic arms for patients with severe motor dysfunction using their own brain signals. However, there have been few studies investigating the decoding accuracy for multiclasses of useful unilateral upper limb movements using non-invasive measurements. We investigated the decoding accuracy for classifying three types of unilateral upper limb movements using single-trial magnetoencephalography (MEG) signals. Neuromagnetic activities were recorded in 9 healthy subjects performing 3 types of right upper limb movements: hand grasping, pinching, and elbow flexion. A support vector machine was used to classify the single-trial MEG signals. The movement types were predicted with an average accuracy of 66 ± 10% (chance level: 33.3%) using neuromagnetic activity during a 400-ms interval (− 200 ms to 200 ms from movement onsets). To explore the time-dependency of the decoding accuracy, we also examined the time course of decoding accuracy in 50-ms sliding windows from − 500 ms to 500 ms. Decoding accuracies significantly increased and peaked once before (50.1 ± 4.9%) and twice after (58.5 ± 7.5% and 64.4 ± 7.6%) movement onsets in all subjects. Significant variability in the decoding features in the first peak was evident in the channels over the parietal area and in the second and third peaks in the channels over the sensorimotor area. Our results indicate that the three types of unilateral upper limb movement can be inferred with high accuracy by detecting differences in movement-related brain activity in the parietal and sensorimotor areas.

In order to clarify whether neurophysiological profiles affect the performance of brain machine interfaces (BMI), we examined the relationships between amplitudes of movement-related cortical fields (MRCFs) and decoding performances during movement. Neuromagnetic activities were recorded in nine healthy participants during three types of unilateral upper limb movements. The movement types were inferred by a support vector machine. The amplitude of MRCF components, motor field (MF), movement-evoked field I (MEFI), and movement-evoked field II (MEFII) were compared with the decoding accuracies in all participants. Decoding accuracies at the latencies of MF, MEFI, and MEFII surpassed the chance level in all participants. In particular, accuracies at MEFI and MEFII were significantly higher in comparison with that of MF. The amplitudes and decoding accuracies were strongly correlated (MF, r(s)=0.90; MEFI, r(s)=0.90; and MEFII, r(s)=0.87). Our results show that the variation of MRCF components among participants reflects decoding performance. Neurophysiological profiles may serve as a predictor of individual BMI performance and assist in the improvement of general BMI performance.

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.

We propose a load sensitive Continuously Variable Transmission (CVT) for a wire-driven robot hand “Drum CVT”, and aims at achieving efficient finger motions by mechanically changing the reduction rate of the drive: fast finger motion with low load (i.e., low drive at fast motion) and slow finger motion with high load (i.e., high drive at slow motion). We developed two material types of Drum CVT: the deflection-type (using nylon) and the torsion-type (using metal). Both types are investigated in both theoretical and actual models, and demonstrated their performance. Eventually, we revealed those characteristics, and indicated the usage of each Drum-CVT.

We propose a novel robotic developmental kit for educational purpose. It helps junior high / high school / university students to understand recent prosthetic technology and, moreover, to provide a chance to produce creative prosthetic applications for short time at low cost. The developmental kit consists of an EMG-to-Motor controller and a wire-driven device. For delivering the cheapness and easiness to students, we demonstrate three prosthetic applications based on the kit: (1) Simple Prosthetic Hand is a mimic of commercial prosthetic hand. It illustrates that low-precise design achieves cheap production cost and sufficient function as a prosthetic hand. (2) Rock-Scissors-Paper Prosthetic Hand is based on prosthetic hands for research use. It clearly illustrates EMG-to-Motion discrimination processes by displaying real signals. (3) EMG Presbyopia Spectacles shows the possibility that beginners design creative prosthetic applications based on daily activities. Finally, we report two educational courses we have conducted for junior high and high school students.

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.

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.

Many legged robots have been developed, and some of them have already achieved dynamically stable bipedal and quadruped locomotion. Common characteristics of these conventional legged robots are that their motions are precisely manipulated by their control (i.e., gait and balance). In the case of ASIMO (Hirose et al., 2001) and HRP2 (Kaneko et al., 2004), their dynamically stable bipedal locomotion is based primarily on the Zero Moment Point (ZMP) concept, which was originally developed by Vukobratovic (Vukobratovic, 1969): the ZMP is a fictitious point on the ground plane, where the torques around the (horizontal) x and y axes (generated by ground reaction forces, inertial forces, and torques) are equal to zero. These robots dynamically stabilize their balance by manipulating the ZMP to remain within the support polygon area defined by their square feet, and their gait control is applied on top of the balance control. As a result, these conventional legged robots require rapid information processing and high-power drives to achieve locomotion. Therefore, they are characterized as having more complex control systems and larger energy consumption than those of biological locomotion. On the other hand, these design requirements (i.e., fast information processing and highpower drives) are regarded as disadvantageous in biologically inspired robotics. In the field, the specific behaviors and/or structures of biological organisms are imitated (Vogel, 1998) (Alexander, 2002) with robotic technology. As a result, the robot systems have simple design requirements compared to conventional robot systems, and achieve complex tasks. Thus, the design effort tends to focus on control complexity and energy requirements. A representative instance is the Passive Dynamic Walker (PDW), which was originally developed by McGeer (McGeer, 1990). The PDW has no controller (i.e., no sensor and no motor), and the structure is based on the physical characteristics of human walking: passive hip joints, latch knee joints, and curved feet. The PDW walks down a slope, and its structure exploits gravity as the driving force. This design principle has been applied to the Cornell Biped (Collins & Ruina, 2005) and Denise at TU Delft (Wisse, 2003), and the powered passive dynamic walkers achieved passive dynamic walking on a flat plane. In a summary of these two approaches, conventional robots require expensive design components to achieve dynamically stable locomotion. On the other hand, biologically inspired robots require physical characteristics that exploit their own dynamics to achieve

Biped walking is one of the most graceful movements observed in humans. Today’s humanoid robots, despite their undeniably impressive performance, are still a long way from the elegance and grace found in Nature. To narrow the gap between natural and artificial systems, we propose to rely more on morphology, intrinsic dynamics, and less on raw computation. This paper documents a series of simulated and real “pseudo-passive” dynamic biped walkers in which computation is traded off for good morphology, that is, adequate mechanical design and appropriate material properties These two factors are parameterized, and the resulting solution space is explored in simulation. Interesting solutions are then realized in the real world. Our experiments show that successful pseudo-passive walkers with a good morphology locomote by converting oscillatory energy into forward movement.

The aim of this work was to comprehensively evaluate the cephalometric features in Japanese patients with obstructive sleep apnoea (OSA) and to elucidate the relationship between cephalometric variables and severity of apnoea. Forty-eight cephalometric variables were measured in 37 healthy males and 114 male OSA patients, who were classed into 54 non-obese (body mass index (BMI) or = 27 kg x m(-2), AHI=45.6+/-28.0 events h(-1)) groups. Diagnostic polysomnography was carried out in all of the OSA patients and in 19 of the normal controls. The non-obese OSA patients showed several cephalometric defects compared with their BMI-matched normal controls: 1) decreased facial A-P distance at cranial base, maxilla and mandible levels and decreased bony pharynx width; 2) enlarged tongue and inferior shift of the tongue volume; 3) enlarged soft palate; 4) inferiorly positioned hyoid bone; and 5) decreased upper airway width at four different levels. More extensive and severe soft tissue abnormalities with a few defects in craniofacial bony structures were found in the obese OSA group. For the non-obese OSA group, the stepwise regression model on AHI was significant with two bony structure variables as determinants: anterior cranial base length (S-N) and mandibular length (Me-Go). Although the regression model retained only linear distance between anterior vertebra and hyoid bone (H-VL) as an explainable determinant for AHI in the obese OSA group, H-VL was significantly correlated with soft tissue measurements such as overall tongue area (Ton), inferior tongue area (Ton2) and pharyngeal airway length (PNS-V). In conclusion, Japanese obstructive sleep apnoea patients have a series of cephalometric abnormalities similar to those described in Caucasian patients, and that the aetiology of obstructive sleep apnoea in obese patients may be different from that in non-obese patients. In obese patients, upper airway soft tissue enlargement may play a more important role in the development of obstructive sleep apnoea, whereas in non-obese patients, bony structure discrepancies may be the dominant contributing factors for obstructive sleep apnoea.

This paper describes a low-noise amplifier with multiple adjustable parameters for neural recording applications. An adjustable pseudo-resistor implemented by cascade metal–oxide–silicon field-effect transistors (MOSFETs) is proposed to achieve low-signal distortion and wide variable bandwidth range. The amplifier has been implemented in 0.18 µm standard complementary metal–oxide–semiconductor (CMOS) process and occupies 0.09 mm2 on chip. The amplifier achieved a selectable voltage gain of 28 and 40 dB, variable bandwidth from 0.04 to 2.6 Hz, total harmonic distortion (THD) of 0.2% with 200 mV output swing, input referred noise of 2.5 µVrms over 0.1–100 Hz and 18.7 µW power consumption at a supply voltage of 1.8 V.