Strategies for Medical Device Development: User and Stakeholder Perceptions.

Medical device development involves user safety, and it is governed by specific regulations. The failure of medical device developers to consider the influence of users, the environment, and related organizations on product development during the design and development process can result in added risks to the use of medical technologies. Although many studies have examined the medical device development process, there has been no systematic and comprehensive assessment of the key factors affecting medical device development. This research synthesized the value of medical device industry stakeholders' experiences through a literature review and interviews with industry experts. Then, it establishes an FIA-NRM model to identify the key factors affecting medical device development and suggests appropriate pathways for improvement. Results indicate that the development of medical devices should begin with stabilizing organizational characteristics, followed by strengthening technical capability and use environment, and finally, consideration should be given to the user action of medical devices. The results provide medical device developers with optimal development pathways and resource allocation recommendations to support developers in developing medical device development strategies as well as ensuring the safety and effectiveness of the products for end users.

Med-Tech Industry Entry Strategy Analysis under COVID-19 Impact.

COVID-19 has been impacting the Med-Tech industry dramatically since the beginning of 2020. Along with the pandemic continuously growing, the demand for major global medical products such as masks and protective clothing has surged. The Med-Tech industry is facing the huge challenge of a lack of production capacity, including raw material, production equipment, production line, professional human resources, and more. It would require not only the operators in the Med-Tech industry to enlarge their productivity, but also new investors from outside. This study focused on the entry strategy analysis of the Med-Tech industry, developing five driving factors, and conducting an opinion survey from three different aspects, including vendors, channels, and end-users, under COVID-19 impact. A total of 99 valid questionnaires were collected. After that, the Importance Accessibility Analysis-Network Relation Map (IAA-NRM) approach was used to verify the importance and implementation priority of the entry strategies. Then, the Decision Making Trial and Evaluation Laboratory (DEMATEL) technique is used to construct the NRM method. The research results showed that there is a common strategic path, from the regulatory system to operation resources and then marketing promotion. In addition, in these three viewpoints, vendors and end-users have similar priorities in terms of industry attributes and barriers to entry.

Innovative Head-Mounted System Based on Inertial Sensors and Magnetometer for Detecting Falling Movements.

This work presents a fall detection system that is worn on the head, where the acceleration and posture are stable such that everyday movement can be identified without disturbing the wearer. Falling movements are recognized by comparing the acceleration and orientation of a wearer's head using prespecified thresholds. The proposed system consists of a triaxial accelerometer, gyroscope, and magnetometer; as such, a Madgwick's filter is adopted to improve the accuracy of the estimation of orientation. Moreover, with its integrated Wi-Fi module, the proposed system can notify an emergency contact in a timely manner to provide help for the falling person. Based on experimental results concerning falling movements and activities of daily living, the proposed system achieved a sensitivity of 96.67% in fall detection, with a specificity of 98.27%, and, therefore, is suitable for detecting falling movements in daily life.

A Real-Time Speech Separation Method Based on Camera and Microphone Array Sensors Fusion Approach.

In the context of assisted human, identifying and enhancing non-stationary speech targets speech in various noise environments, such as a cocktail party, is an important issue for real-time speech separation. Previous studies mostly used microphone signal processing to perform target speech separation and analysis, such as feature recognition through a large amount of training data and supervised machine learning. The method was suitable for stationary noise suppression, but relatively limited for non-stationary noise and difficult to meet the real-time processing requirement. In this study, we propose a real-time speech separation method based on an approach that combines an optical camera and a microphone array. The method was divided into two stages. Stage 1 used computer vision technology with the camera to detect and identify interest targets and evaluate source angles and distance. Stage 2 used beamforming technology with microphone array to enhance and separate the target speech sound. The asynchronous update function was utilized to integrate the beamforming control and speech processing to reduce the effect of the processing delay. The experimental results show that the noise reduction in various stationary and non-stationary noise environments were 6.1 dB and 5.2 dB respectively. The response time of speech processing was less than 10ms, which meets the requirements of a real-time system. The proposed method has high potential to be applied in auxiliary listening systems or machine language processing like intelligent personal assistant.