Scalably Nanomanufactured Atomically Thin Materials‐Based Wearable Health Sensors

Blockchain is a technology to enable decentralized collaboration among un-trusted entities. Academia and industry are rushing to uncover its potential for their field of interest. Due to the novelty of the technology and its diverse applications, there are some ambiguities in approaches and trends. In this paper, we analyze the scientific publications and patents from the past five years to identify the trends for blockchain integration with healthcare. For this purpose, we have adopted a quantitative (clustering) and qualitative (theme extraction) approach to discover themes and temporal dynamics in academia and industry. Our results shed light on the potential challenges and vision for future works.

Blockchain Technology in Healthcare: A Scientific and Technological Driving Force

While sports activity recognition is a well studied subject in mobile, wearable and ubiquitous computing, work to date mostly focuses on recognition and counting of specific exercise types. Quality assessment is a much more difficult problem with significantly less published results. In this work, we present Quali-Mat: a method for evaluating the quality of execution (QoE) in exercises using a smart sports mat that can measure the dynamic pressure profiles during full-body, body-weight exercises. As an example, our system not only recognizes that the user is doing push-ups, but also distinguishes 5 subtly different types of push-ups, each of which (according to sports science literature and professional trainers) has a different effect on different muscle groups. We have investigated various machine learning algorithms targeting the specific type of spatio-temporal data produced by the pressure mat system. We demonstrate that computationally efficient, yet effective Conv3D model outperforms more complex state-of-the-art options such as transfer learning from the image domain. The approach is validated through an experiment designed to cover 47 quantifiable variants of 9 basic exercises with 12 participants. Overall, the model can categorize 9 exercises with 98.6% accuracy / 98.6% F1 score, and 47 QoE variants with 67.3% accuracy / 68.1% F1 score. Through extensive discussions with both the experiment results and practical sports considerations, our approach can be used for not only precisely recognizing the type of exercises, but also quantifying the workout quality of execution on a fine time granularity. We also make the Quali-Mat data set available to the community to encourage further research in the area.

Quali-Mat

Mobile and wearable technologies have promised significant changes to the healthcare industry. Although cutting-edge communication and cloud-based technologies have allowed for these upgrades, their implementation and popularization in low-income countries have been challenging. We propose ODSearch , an On-device Search framework equipped with a natural language interface for mobile and wearable devices. To implement search, ODSearch employs compression and Bloom filter, it provides near real-time search query responses without network dependency. In particular, the Bloom filter reduces the temporal scope of the search and compression reduces the size of the data to be searched. Our experiments were conducted on a mobile phone and smartwatch. We compared ODSearch with current state-of-the-art search mechanisms, and it outperformed them on average by 53 times in execution time, 26 times in energy usage, and 2.3% in memory utilization . CCS Concepts: • Information systems → on mobile devices .

ODSearch: Fast and Resource Efficient On-device Natural Language Search for Fitness Trackers' Data

Application of intelligent real-time image processing in fitness motion detection under internet of things

There is growing consumer demand for digital technologies that help users track, motivate, and receive coaching for both aerobic and anaerobic activities. In this article, we provide a review of existing technological advances in tracking, coaching, and motivating users during indoor training in contexts such as gymnasiums. This study lists the advantages and limitations of various apparatuses and applications used for this purpose. Our review and discussion are intended to help entrepreneurs and engineers improve their products to better meet users? needs and aid researchers in identifying potential new areas.

Advances for Indoor Fitness Tracking, Coaching, and Motivation: A Review of Existing Technological Advances

The role of wearable technology in our daily lives is rapidly growing and many users are cumulatively becoming dependent on it. To provide insight into the future of wearable technologies and various community attitudes towards them, we implemented an in-depth quantitative investigation of opinions from academic texts (DBLP and PubMed), social media (Twitter), news media (Google News and Bing News), and entrepreneurship communities (Kickstarter and Indiegogo) over a 10-year period. Our results indicate that unlike academia, the news media, entrepreneurship communities, and social media all hold overall positive attitudes towards wearable technologies. Secondly, there are diverse perspectives towards various wearable products across different platforms. Specifically, "XR" technologies received the most attention, while "Exoskeleton" ignited the most heated debates. Thirdly, we discovered that the lifetime of a hyped wearable technology lasts approximately three years. Furthermore, the news media and entrepreneurship community's attitudes towards wearable technologies did not have a strong impact on public opinion. Finally, among all types of wearable technologies, "fashion design" and "healthcare" products were the most enlightening for the market.

11 Years with Wearables: Quantitative Analysis of Social Media, Academia, News Agencies, and Lead User Community from 2009-2020 on Wearable Technologies

Yanglei Gan

Papers

Advances for Indoor Fitness Tracking, Coaching, and Motivation: A Review of Existing Technological Advances

11 Years with Wearables: Quantitative Analysis of Social Media, Academia, News Agencies, and Lead User Community from 2009-2020 on Wearable Technologies