Toyota Jidosha Kabushiki Kaisha

Riken Corporation

Shingo Shimoda

Hitoshi Yamada

Takashi Izuo

Masashi Yamashita

Hisayoshi Sugihara

Keiichi Kitajo

Hidenori Kimura

Tytus Wojtara

Masaaki Yamaoka

Shintaro Yoshizawa

Eisuke Aoki

Hiroshi Yamasaki

Soya Takagi

Hitoshi Konosu

Hideki Kajima

Alvaro Costa Garcia

Fady S K Shibata Alnajjar

Matti Sakari Itkonen

Fady Sk Shibata Alnajjar

Vincent Berenz

A rehabilitation evaluation apparatus includes a sensor signal acquisition unit configured to acquire a sensor signal output from a detection sensor, a selection unit configured to select at least one myoelectric signal having a correlation with the sensor signal acquired by the sensor signal acquisition unit from among the plurality of second myoelectric signals on the second-side part acquired by the myoelectric-signal acquisition unit, and a similarity output unit configured to select a first myoelectric signal that has been output from a myoelectric sensor attached in a place that is left-right symmetric to a place of the myoelectric sensor that has output the second correlated myoelectric signal selected by the selection unit from among a plurality of first myoelectric signals on the first-side part acquired by the myoelectric-signal acquisition unit, calculate a similarity between these correlated myoelectric signals, and outputs the calculated similarity.

Rehabilitation evaluation apparatus, rehabilitation evaluation method, and rehabilitation evaluation program

An upper limb rehabilitation support device includes a first handle that is connected with a first rotating shaft, gripped by a trainee's right hand and rotated, a second handle that is connected with a second rotating shaft, gripped by a trainee's left hand and rotated, a connecting part structured so as to connect the first rotating shaft and the second rotating shaft with each other and interlock rotations of the first handle and the second handle, and a switching part structured so as to switch directions of the rotations of the first handle and the second handle, which are interlocked by the connecting part, with respect to the other handles when one of the first handle and the second handle rotates.

Upper limb rehabilitation support device

A training apparatus includes: a controller. The controller is configured to: (a) while a subject is performing a behavior, acquire time-series myoelectric potentials of each of muscles of the subject; (b) calculate a muscle synergy matrix W, a control matrix C and an error matrix E from a myoelectric potential matrix M so that the relationship M=WC+E is satisfied, row vectors each expressing the acquired time-series myoelectric potentials of a corresponding one of the muscles, m being arranged in rows in the myoelectric potential matrix, n unit column vectors being arranged in columns in the muscle synergy matrix W, n row vectors being arranged in rows in the control matrix; (c) calculate a feature quantity indicating consistency among the unit column vectors included in the calculated muscle synergy matrix W; and (d) output the calculated feature quantity.

Training apparatus, training method, and program

An upper-limb rehabilitation assisting device includes first and second handles coupled to first and second rotating shafts and rotationally operated by hands on a paralytic limb side and a healthy limb side; first and second biosignal detecting parts that detect first and second biosignals corresponding to the paralytic limb side and the healthy limb side; first and second drive parts that drive the first and second rotating shafts; and a control part that performs a cooperative control of the first rotating shaft and the second rotating shaft. The control part controls the torques of the first and second drive parts at the time of the cooperative control of the first and second rotating shafts the basis of the degree of cooperation between the first and second biosignals.

Upper-limb rehabilitation assisting device and method for controlling the same

A rehabilitation system that assists an action of a paretic arm due to brain damage, such as a stroke, includes: a detecting unit configured to detect an assist action that a healthy arm assists the paretic arm; an assist unit configured to cause the paretic arm to carry out bending and stretching actions; an adjustment unit configured to adjust an operation timing, operation speed, bending load or stretching load of the assist unit in response to detection of the assist action by the detecting unit; and a control unit configured to cause the assist unit to operate in accordance with the operation timing, operation speed, bending load or stretching load, adjusted by the adjustment unit.

Rehabilitation system

A device that supports a motion for restoring an inclination angle of a body to a reference direction is provided. A balance device includes a sensor, at least one flywheel, and a controller. The sensor is configured to detect an inclination angle of the body with respect to the reference direction. The at least one flywheel is arranged on the balance device so that an axis of the flywheel is non-parallel to a yaw axis of the body when the balance device is attached to a user. The yaw axis of the body corresponds to a longitudinal direction of the body. In addition, the yaw axis coincides with the reference direction when the user stands erect. The controller is configured to change a rotation rate of the flywheel based on an inclination angle detected by the sensor.

Balance device

Using variable neuron thresholds and extended Hebb's rule in a neural network, a neuron device for simulating a nerve cell includes a threshold storage unit storing a threshold variable &#x3b8; and threshold coefficients &#x394;&#x3b8;and &#x394;&#x3b8;; an input reception unit receiving one or more input signal values at predetermined time intervals; an output unit outputting an output signal value '1' indicating that the neuron device is firing when the sum total s of received input signal values is equal to or greater than the value of the stored threshold variable &#x3b8;, or a value '0' indicating that the neuron device is resting; and a threshold updating unit calculating &#x394;&#x3b8;X+&#x394;&#x3b8;(X&#x2212;1) using the output signal value X and the stored threshold coefficients &#x394;&#x3b8;and &#x394;&#x3b8;and updating the value of the threshold variable &#x3b8; stored in the threshold storage unit by increasing it by the calculation result.

Neuron device for simulating a nerve cell and neural network device, integer cluster device, feedback control device, and computer program product thereof

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Kasugai, Japan

Shingo Shimoda