HybMED: A Hybrid Neural Network Training Processor With Multi-Sparsity Exploitation for Internet of Medical Things.

Cloud-based training and edge-based inference modes for Artificial Intelligence of Medical Things (AIoMT) applications suffer from accuracy degradation due to physiological signal variations among patients. On-chip learning can overcome this issue by online adaptation of neural network parameters for user-specific tasks. However, existing on-chip learning processors have limitations in terms of versatility, resource utilization, and energy efficiency.

Minimally Invasive Hypoglossal Nerve Stimulator Enabled by ECG Sensor and WPT to Manage Obstructive Sleep Apnea.

A hypoglossal nerve stimulator (HGNS) is an invasive device that is used to treat obstructive sleep apnea (OSA) through electrical stimulation. The conventional implantable HGNS device consists of a stimuli generator, a breathing sensor, and electrodes connected to the hypoglossal nerve via leads. However, this implant is bulky and causes significant trauma.

A 510 μW 0.738-mm 6.2-pJ/SOP Online Learning Multi-Topology SNN Processor With Unified Computation Engine in 40-nm CMOS.

Implementing neural networks (NN) on edge devices enables AI to be applied in many daily scenarios. The stringent area and power budget on edge devices impose challenges on conventional NNs with massive energy-consuming Multiply Accumulation (MAC) operations and offer an opportunity for Spiking Neural Networks (SNN), which can be implemented within sub-mW power budget. However, mainstream SNN topologies varies from Spiking Feedforward Neural Network (SFNN), Spiking Recurrent Neural Network (SRNN), to Spiking Convolutional Neural Network (SCNN), and it is challenging for the edge SNN processor to adapt to different topologies.

SpikeSEE: An energy-efficient dynamic scenes processing framework for retinal prostheses.

Intelligent and low-power retinal prostheses are highly demanded in this era, where wearable and implantable devices are used for numerous healthcare applications. In this paper, we propose an energy-efficient dynamic scenes processing framework (SpikeSEE) that combines a spike representation encoding technique and a bio-inspired spiking recurrent neural network (SRNN) model to achieve intelligent processing and extreme low-power computation for retinal prostheses. The spike representation encoding technique could interpret dynamic scenes with sparse spike trains, decreasing the data volume.

A 0.99-to-4.38 uJ/class Event-Driven Hybrid Neural Network Processor for Full-Spectrum Neural Signal Analyses.

Versatile and energy-efficient neural signal processors are in high demand in brain-machine interfaces and closed-loop neuromodulation applications. In this paper, we propose an energy-efficient processor for neural signal analyses. The proposed processor utilizes three key techniques to efficiently improve versatility and energy efficiency.

Artificial intelligence techniques for retinal prostheses: a comprehensive review and future direction.

. Retinal prostheses are promising devices to restore vision for patients with severe age-related macular degeneration or retinitis pigmentosa disease. The visual processing mechanism embodied in retinal prostheses play an important role in the restoration effect.

MSPAN: A Memristive Spike-Based Computing Engine With Adaptive Neuron for Edge Arrhythmia Detection.

In this work, a memristive spike-based computing in memory (CIM) system with adaptive neuron (MSPAN) is proposed to realize energy-efficient remote arrhythmia detection with high accuracy in edge devices by software and hardware co-design. A multi-layer deep integrative spiking neural network (DiSNN) is first designed with an accuracy of 93.6% in 4-class ECG classification tasks.

EMG-Centered Multisensory Based Technologies for Pattern Recognition in Rehabilitation: State of the Art and Challenges.

In the field of rehabilitation, the electromyography (EMG) signal plays an important role in interpreting patients' intentions and physical conditions. Nevertheless, utilizing merely the EMG signal suffers from difficulty in recognizing slight body movements, and the detection accuracy is strongly influenced by environmental factors. To address the above issues, multisensory integration-based EMG pattern recognition (PR) techniques have been developed in recent years, and fruitful results have been demonstrated in diverse rehabilitation scenarios, such as achieving high locomotion detection and prosthesis control accuracy.

[Disaster nursing and primary school teachers' disaster-related healthcare knowledge and skills].

The World Bank has ranked Taiwan as the 5th highest risk country in the world in terms of full-spectrum disaster risk. With volatile social, economic, and geologic environments and the real threat of typhoons, earthquakes, and nuclear disasters, the government has made a public appeal to raise awareness and reduce the impact of disasters. Disasters not only devastate property and the ecology, but also cause striking and long-lasting impacts on life and health.

Talar body fracture combined with traumatic rupture of anterior talofibular ligament and peroneal longus tendon.

A rare case of talar body fracture combined with traumatic rupture of the anterior talofibular ligament and peroneal longus tendon is presented and reports in the literature are reviewed. We suggest that the mechanism of the injury was initial plantar flexion and inversion with rupture of anterior talofibular ligament and peroneal longus tendon, followed by forced dorsiflexion with talar body fracture. The treatment consisted of open reduction with internal fixation of the talar body fracture and primary repairs of the ruptured anterior talofibular ligament and peroneal longus tendon.