A Novel Implementation of a Social Robot for Sustainable Human Engagement in Homecare Services for Ageing Populations.

This research addresses the rapid aging phenomenon prevalent in Asian societies, which has led to a significant increase in elderly individuals relocating to nursing homes due to health-related issues. This trend has resulted in social isolation and loneliness among the elderly, contributing to physical and mental ailments such as hypertension and cardiovascular diseases, as highlighted by the World Health Organization (WHO). To mitigate these issues, the research proposes leveraging technology, specifically the integration of robotics, to alleviate the caregiver shortage and enhance human interaction for the elderly. The novel approach involves developing a social robot designed to bridge the gap between humans and machines, combining knowledge from embedded systems, robotics, and essential soft skills for effective interaction. The authors found that this technological solution holds promise in addressing the caregiver shortage and improving the well-being of elderly individuals by reducing their sense of isolation, fostering better mental and physical health outcomes, and potentially transforming the landscape of elderly care through innovative technological applications. Future work includes expanding pilot studies and collaborating with healthcare institutions to further validate the effectiveness of the solution.

Pre-Computing Batch Normalisation Parameters for Edge Devices on a Binarized Neural Network.

Binarized Neural Network (BNN) is a quantized Convolutional Neural Network (CNN), reducing the precision of network parameters for a much smaller model size. In BNNs, the Batch Normalisation (BN) layer is essential. When running BN on edge devices, floating point instructions take up a significant number of cycles to perform. This work leverages the fixed nature of a model during inference, to reduce the full-precision memory footprint by half. This was achieved by pre-computing the BN parameters prior to quantization. The proposed BNN was validated through modeling the network on the MNIST dataset. Compared to the traditional method of computation, the proposed BNN reduced the memory utilization by 63% at 860-bytes without any significant impact on accuracy. By pre-computing portions of the BN layer, the number of cycles required to compute is reduced to two cycles on an edge device.

A Non-invasive Real-time Localization System for Enhanced Efficacy in Nasogastric Intubation.

Nasogastric (NG) intubation is one of the most commonly performed clinical procedures. Real-time localization and tracking of the NG tube passage at the larynx region into the esophagus is crucial for safety, but is lacking in current practice. In this paper, we present the design, analysis and evaluation of a non-invasive real-time localization system using passive magnetic tracking techniques to improve efficacy of the clinical NG intubation process. By embedding a small permanent magnet at the insertion tip of the NG tube, a wearable system containing embedded sensors around the neck can determine the absolute position of the NG tube inside the body in real-time to assist in insertion. In order to validate the feasibility of the proposed system in detecting erroneous tube placement, typical reference intubation trajectories are first analyzed using anatomically correct models and localization accuracy of the system are evaluated using a precise robotic platform. It is found that the root-mean-squared tracking accuracy is within 5.3 mm for both the esophagus and trachea intubation pathways. Experiments were also designed and performed to demonstrate that the system is capable of tracking the NG tube accurately in biological environments even in presence of stationary ferromagnetic objects (such as clinical instruments). With minimal physical modification to the NG tube and clinical process, this system allows accurate and efficient localization and confirmation of correct NG tube placement without supplemental radiographic methods which is considered the current clinical standard.