Abstract: An adaptive controller used in a photoplethysmography sensing system, comprises a plurality of hardware circuits which are configured to: receive a photoplethysmography signal (hereinafter, “PPG signal”) processed; determine whether the PPG signal processed satisfies with a requirement; output the PPG signal processed if the PPG signal processed satisfies with a requirement; and adjust a gain of an amplifier for amplifying the PPG signal and/or a driving signal of a light source if the PPG signal processed does not satisfy with a requirement.

Abstract: A display device including at least two light source modules and a display control substrate is provided. Each of the at least two light source substrates has a first surface and a second surface opposite to each other and includes a plurality of light emitting elements and a plurality of connection pads. The light emitting elements are located on the second surface, and the connection pads are located on the first surface and are electrically connected to the light emitting elements. The display control substrate includes a back plate and a plurality of control elements. The control elements are located on the back plate, part of the control elements are electrically connected to the connection pads to drive and control the light emitting elements, and the second surface of each of the at least two light source substrates forms a part of a display surface of the display device.

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: An automatic control method of a mechanical arm and an automatic control system are provided. The automatic control method includes the following steps: obtaining a color image and depth information corresponding to the color image through a depth camera; performing image space cutting processing and image rotation processing according to the color image and the depth information to generate a plurality of depth images; inputting the depth images into an environmental image recognition module such that the environmental image recognition module outputs a displacement coordinate parameter; and outputting the displacement coordinate parameter to a mechanical arm control module such that the mechanical arm control module controls the mechanical arm to move according to the displacement coordinate parameter.

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: A method for measuring spasticity is provided and includes: obtaining sensing signals corresponding to a limb movement through at least one sensor during a period of time; transforming the sensing signals into a two-dimensional image; and inputting the two-dimensional image into a convolutional neural network to output a spasticity determination result.

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: A surgical instrument inventory system, including a memory and a processor, is provided. The memory is configured to store a global identification module and a local identification module. The processor is coupled to the memory and is configured to input a surgical instrument image to the global identification module and the local identification module. The global identification module is configured to output multiple global image features corresponding to the surgical instrument image. The local identification module is configured to output multiple local image features corresponding to an instrument end image of the surgical instrument image. The processor determines a surgical instrument type of the surgical instrument image according to the multiple global image features and the multiple local image features.

Abstract: An automatic control method of a mechanical arm and an automatic control system are provided. The automatic control method includes the following steps: obtaining a color image and depth information corresponding to the color image through a depth camera; performing image space cutting processing and image rotation processing according to the color image and the depth information to generate a plurality of depth images; inputting the depth images into an environmental image recognition module such that the environmental image recognition module outputs a displacement coordinate parameter; and outputting the displacement coordinate parameter to a mechanical arm control module such that the mechanical arm control module controls the mechanical arm to move according to the displacement coordinate parameter.

Abstract: An adaptive controller used in a photoplethysmography sensing system, comprises a plurality of hardware circuits which are configured to: receive a photoplethysmography signal (hereinafter, “PPG signal”) processed; determine whether the PPG signal processed satisfies with a requirement; output the PPG signal processed if the PPG signal processed satisfies with a requirement; and adjust a gain of an amplifier for amplifying the PPG signal and/or a driving signal of a light source if the PPG signal processed does not satisfy with a requirement.

Abstract: A medical image modeling system and a medical image modeling method are provided. The medical image modeling system includes an input device, a processing device, and a storage device. The input device obtains medical image data. The processing device is coupled to the input device. The processing device converts the medical image data into a bone model in response to a first operational command. The storage device is coupled to the processing device. The storage device stores a model database. The processing device reads the model database in response to a second operational command to obtain a bone reconstruction block corresponding to a defect block of the bone model. The processing device integrates a bone reconstruction model depicted corresponding to the bone reconstruction block into the defect block of the bone model to generate an integrated bone model.

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 adaptive controller used in a photoplethysmography sensing system, comprises a plurality of hardware circuits which are configured to: receive a photoplethysmography signal (hereinafter, “PPG signal”) processed; determine whether the PPG signal processed satisfies with a requirement; output the PPG signal processed if the PPG signal processed satisfies with a requirement; and adjust a gain of an amplifier for amplifying the PPG signal and/or a driving signal of a light source if the PPG signal processed does not satisfy with a requirement.

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: A method for measuring spasticity is provided and includes: obtaining sensing signals corresponding to a limb movement through at least one sensor during a period of time; transforming the sensing signals into a two-dimensional image; and inputting the two-dimensional image into a convolutional neural network to output a spasticity determination result.

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.

Abstract: A medical image modeling system and a medical image modeling method are provided. The medical image modeling system includes an input device, a processing device, and a storage device. The input device obtains medical image data. The processing device is coupled to the input device. The processing device converts the medical image data into a bone model in response to a first operational command. The storage device is coupled to the processing device. The storage device stores a model database. The processing device reads the model database in response to a second operational command to obtain a bone reconstruction block corresponding to a defect block of the bone model. The processing device integrates a bone reconstruction model depicted corresponding to the bone reconstruction block into the defect block of the bone model to generate an integrated bone model.

Abstract: The present invention discloses a method for finding a cell nucleus of a target cell from a cell image, wherein the cell image includes the target cell and at least one variation cell, and the target cell includes cytoplasm and the cell nucleus. The method includes steps of: (a) processing the cell image via an image processor such that the cytoplasm, the cell nucleus and the variation cell have different shades of color; (b) demarcating the outlines of the cytoplasm, the cell nucleus and the variation cell; (c) calculating geometrical reference points of the outlines; (d) calculating the distances from the geometrical reference point of the cytoplasm outline to the geometrical reference point of the cell nucleus outline and to the geometrical reference points of the variation cell outlines; and (e) finding a specific geometrical reference point having a shortest distance to locate a specific outline corresponding to the specific geometrical reference point as the cell nucleus.

Abstract: An adaptive controller used in a photoplethysmography sensing system, comprises a plurality of hardware circuits which are configured to: receive a photoplethysmography signal (hereinafter, “PPG signal”) processed; determine whether the PPG signal processed satisfies with a requirement; output the PPG signal processed if the PPG signal processed satisfies with a requirement; and adjust a gain of an amplifier for amplifying the PPG signal and/or a driving signal of a light source if the PPG signal processed does not satisfy with a requirement.