Abstract: A surgical robotic arm control system and a control method thereof are provided. The surgical robotic arm control system includes a surgical robotic arm, an image capturing unit, and a processor. The surgical robotic arm has multiple joint axes. The image capturing unit obtains a first image. The processor executes a spatial environment recognition module to generate a first environment information image, a first direction information image, and a first depth information image according to the first image. The processor executes a spatial environment image processing module to calculate path information according to the first environment information image, the first direction information image, and the first depth information image. The processor executes a robotic arm motion feedback module to operate the surgical robotic arm to move according to the path information.

Abstract: A surgical robotic arm control system and a surgical robotic arm control method are provided. The surgical robotic arm control system includes a surgical robotic arm, a first image capturing unit, a second image capturing unit, and a processor. The first image capturing unit is used for obtaining a field image. The field image includes a first target image of a target object. The second image capturing unit is disposed at an end position of the surgical robotic arm and is used to obtain a second target image of the target object. The processor analyzes the field image to obtain robotic arm movement information, and controls the surgical robotic arm to move to approach the target object. The processor analyzes the target image to obtain robotic arm rotation information, and controls an angle and a posture of the surgical robotic arm.

Abstract: An elbow joint rehabilitation system and an elbow joint rehabilitation method are provided. The elbow joint rehabilitation system includes a support member, a motor, a torque sensing unit, a first electromyography sensor, a second electromyography sensor and a motor control device. In the elbow joint rehabilitation method, the support member is configured to support an arm of a patient. Thereafter, the torque sensing unit is configured to sense the torque applied on the support member to obtain a sensed arm torque signal. Then, the first electromyography sensor and the second electromyography sensor are configured to sense the muscle activities of biceps and triceps of the patient to obtain electromyography signals. Thereafter, the motor is controlled to drive the support member to perform rehabilitation in accordance with the sensed arm torque signal, the electromyography signals and a current support member position provided by the motor.

Abstract: An elbow joint rehabilitation system and an elbow joint rehabilitation method are provided. The elbow joint rehabilitation system includes a support member, a motor, a torque sensing unit, a first electromyography sensor, a second electromyography sensor and a motor control device. In the elbow joint rehabilitation method, the support member is configured to support an arm of a patient. Thereafter, the torque sensing unit is configured to sense the torque applied on the support member to obtain a sensed arm torque signal. Then, the first electromyography sensor and the second electromyography sensor are configured to sense the muscle activities of biceps and triceps of the patient to obtain electromyography signals. Thereafter, the motor is controlled to drive the support member to perform rehabilitation in accordance with the sensed arm torque signal, the electromyography signals and a current support member position provided by the motor.

Abstract: An image analyzing method is provided and includes: extracting a first feature vector according to global information of a digital image; dividing the digital image into multiple regions, and inputting each region into a convolutional neural network to obtain a second feature vector; merging the first feature vector with the second feature vectors to obtain a third feature vector; and performing an image analyzing process according to the third feature vector.

Abstract: An image analyzing method is provided and includes: extracting a first feature vector according to global information of a digital image; dividing the digital image into multiple regions, and inputting each region into a convolutional neural network to obtain a second feature vector; merging the first feature vector with the second feature vectors to obtain a third feature vector; and performing an image analyzing process according to the third feature vector.

Abstract: An image recognition method is provided and includes: decoding a digital image file to obtain a digital image; providing, by a front end application, a user interface so that a user interacts with the user interface through a browser, in which the user interface renders the digital image; receiving an image editing operation corresponding to the digital image through the user interface; obtaining characteristic information corresponding to a sample region of the digital image from the digital image file according to the image editing operation; transmitting, by the front end application, the characteristic information to a server to perform an image recognition procedure.