Abstract: One embodiment of the present invention sets forth an integrated circuit. The integrated circuit includes a plurality of subunits associated with a plurality of operating voltages. The integrated circuit also includes one or more voltage regulator circuits that convert a first input voltage into a first plurality of output voltages during a first test, wherein the plurality of output voltages is delivered to the plurality of subunits via a plurality of output channels.

Abstract: A rehabilitation system based on brainwave control includes a rehabilitation device, a brainwave device and a control unit. The rehabilitation device includes a power unit and a rehabilitation unit. The control unit is coupled with the rehabilitation device and the brainwave device. In a state where the power unit provides the predetermined output to control the rehabilitation unit to drive the rehabilitation part to move, the control unit receives the brainwave signal and determines whether the brainwave signal is lower than a stimulation threshold. In a state where the brainwave signal is lower than the stimulation threshold, the control unit sends an adjustment signal to control the power unit to adjust the predetermined output to drive the rehabilitation unit to move.

Abstract: A lower limb rehabilitation system based on augmented reality and a brain computer interface includes a display, a plurality of motion sensors, a brain wave monitor, and an analysis platform. The display is configured to receive and play a virtual scene video to guide a user to perform gait rehabilitation training. The plurality of motion sensors is configured to sense gait data. The brain wave monitor is configured to record an electroencephalogram signal by detecting an electric current change in a brain wave of the user. The analysis platform is configured to compare the gait data with the virtual scene video to determine the accuracy of footsteps of the user and provide feedback. The analysis platform inputs the electroencephalogram signal to a machine learning model to quantify the electroencephalogram signal into an index value representing a lower limb motor function of the user.

Abstract: In order to expedite testing (such as silicon chip testing), a test pattern that indicates a timing, order, and frequency (e.g., speed) of signals sent during the test may be divided into different portions. Also, a frequency at which each portion of the test pattern is to be run is determined. Each portion is run at a frequency that can be supported by only that portion. As a result, the slowest portion of the test pattern only limits the frequency at which its portion is run, while other portions are run at a faster frequency. This reduces a time taken to run the test pattern in a testing environment.

Abstract: A smart system for monitoring a cognition status of a user includes an electroencephalography (EEG) acquisition module, configured to capture a plurality of brainwave signals; an analog filter circuit, coupled to the EEG acquisition module and configured to amplify the plurality of brainwave signals; an analog to digital (ADC) circuit, coupled to the analog filter circuit and configured to transform the plurality of brainwave signals into a plurality of digital signals; and a digital signaling processing (DSP) circuit, configured to retrieve a plurality of characteristics from the plurality of digital signals to determine the cognition status of the user.

Abstract: A lower limb rehabilitation system includes an analysis platform, a smart insole, a motion sensor and a brain wave sensor. The analysis platform has a deep learning model. The smart insole generates plural pressure signals through plural pressure sensors of a pressure sensing film. The smart insole includes a processing unit controlling a transmission unit to transmit the pressure signals to the analysis platform. The processing unit is connected to a power supply unit. The motion sensor generates a motion signal. The brain wave sensor is coupled with the analysis platform and detects a brain wave signal. The analysis platform inputs the pressure signals, the motion signal and the brain wave signal into the deep learning model for analyzing a gait. The deep learning model analyzes whether the gait is correct. The analysis platform generates a warning message if the gait is analyzed to be incorrect.

Abstract: A system for controlling a robot by brain electrical signal, includes a screen, an electronic signal detection device, and a host computer. The screen shows a plurality of icons thereon, and the plurality of icons flashes at different frequencies. The electrical signal detection device detects brain electrical signal when one of the plurality of icons is stared. The host computer stores a plurality of personal reference parameters corresponding to the plurality of icons of the screen. The host computer processes the brain electrical signal to get parameters of use, and compares the parameters of use with the plurality of personal reference parameters to choose and execute the icon which is stared.

Abstract: A system for controlling a robot by brain electrical signal, includes a screen, an electronic signal detection device, and a host computer. The screen shows a plurality of icons thereon, and the plurality of icons flashes at different frequencies. The electrical signal detection device detects brain electrical signal when one of the plurality of icons is stared. The host computer stores a plurality of personal reference parameters corresponding to the plurality of icons of the screen. The host computer processes the brain electrical signal to get parameters of use, and compares the parameters of use with the plurality of personal reference parameters to choose and execute the icon which is stared.

Abstract: A device of drowsiness detection and alarm includes a headrest, at least one brainwave sensor, a computing unit, a controlling unit and an accessory unit. The brainwave sensor is disposed on the headrest and configured to contact with a user's head and detect a brainwave signal. The computing unit receives the brainwave signal from the brainwave sensor and calculates according to the brainwave signal to obtain a fatigue index. The controlling unit controls the accessory unit to activate according to the fatigue index. Therefore, when the user is drowsy and gradually loses consciousness, the accessory unit can immediately alarm the user and allow the user stay awake to avoid the occurrence of accidents. A method of drowsiness detection and alarm is also disclosed.

Abstract: A system for detecting a core body temperature includes a detection unit, an ECG wave-filter, a body-temperature detection unit, a processing unit, a breath computing and processing unit, a heart-beat computing and processing unit, and a core body temperature computing and processing unit. The detection unit senses the body, and then the ECG wave-filter and body-temperature detection unit measures the electrical cardiac signal and the shell temperature, respectively. The processing unit collects the signals generated by the ECG wave-filter and body-temperature detection unit and transmits the collected signals to the breath computing and processing unit and the heart-beat computing and processing unit, which generate the core body temperature according to the received signals. Accordingly, it is possible to increase the physical parameters for monitoring the vital signs comprehensively. In addition, a method for detecting a core body temperature is also disclosed.

Abstract: The present invention discloses an automatic bio-signal supervising system for medical care. The automatic bio-signal supervising system utilizes a head-mounted bio-signal acquisition device, wore upon the user's head, to acquire the bio-signals, convert them into the digital bio-signals, and transmit the digital bio-signals to a signal analysis processor to perform analysis process. And the automatic bio-signal supervising system delivers a corresponding control signal to the environment control equipment based on the result of analysis, and control the environment condition to achieve the remote supervising and medical care purpose. Furthermore, the signal analysis processor in the present invention further provides a real-time warning signal or a health index, which supervise the personal biological information to avoid the unpredictable situation.