Feasibility of Using Wearable EMG Armbands combined with Unsupervised Transfer Learning for Seamless Myoelectric Control

Myoelectric control uses electromyography (EMG) signals as human-originated input to enable intuitive interfaces with machines. As such, recent rehabilitation robotics employs myoelectric control to autonomously classify user intent or operation mode using machine learning. However, performance in such applications inherently suffers from the non-stationarity of EMG signals across measurement conditions. Current laboratory-based solutions rely on careful, time-consuming control of the recordings or periodic recalibration, impeding real-world deployment. We propose that robust yet seamless myoelectric control can be achieved using a low-end, easy-to-don and doff wearable EMG sensor combined with unsupervised transfer learning. Here, we test the feasibility of one such application using a consumer-grade sensor (Myo armband, 8 EMG channels @ 200 Hz) for gesture classification across measurement conditions using an existing dataset: 5 users x 10 days x 3 sensor locations. Specifically, we first train a deep neural network using Temporal-Spatial Descriptors (TSD) with labeled source data from any particular user, day, or location. We then apply the Self-Calibrating Asynchronous Domain Adversarial Neural Network (SCADANN), which automatically adjusts the trained TSD to improve classification performance for unlabeled target data from a different user, day, or sensor location. Compared to the original TSD, SCADANN improves accuracy by 12±5.2% (avg±sd), 9.6±5.0%, and 8.6±3.3% across all possible user-to-user, day-to-day, and location-to-location cases, respectively. In one best-case scenario, accuracy improves by 26% (from 67% to 93%), whereas sometimes the gain is modest (e.g., from 76% to 78%). We also show that the performance of transfer learning can be improved by using a better model trained with good (e.g., incremental) source data. We postulate that the proposed approach is feasible and promising and can be further tailored for seamless myoelectric control of powered prosthetics or exoskeletons.

Identification of the Most Suitable App to Support the Self-Management of Hypertension: Systematic Selection Approach and Qualitative Study

Background Smartphone apps are increasingly being used to aid in hypertension self-management, and a large and ever-growing number of self-management apps have been commercially released. However, very few of these are potentially effective and secure, and researchers have yet to establish the suitability of specific hypertension apps to particular contexts. Objective The aim of this study is to identify the most suitable hypertension app in the context of Saudi Arabia and its health system. Methods This study used a 2-stage approach to selecting the most suitable app for hypertension self-management. First, a systematic selection approach was followed to identify a shortlist of the most suitable apps according to the criteria of potential effectiveness, theoretical underpinning, and privacy and security. Second, an exploratory qualitative study was conducted to select the most suitable from the shortlist: 12 doctors were interviewed, and 22 patients participated in 4 focus groups. These explored participants’ attitudes towards self-management apps in general, and their views towards the apps identified via the systematic selection process. The qualitative data were analyzed using framework analysis. Results In the first stage, only 5 apps were found to be potentially effective while also having a theoretical underpinning and protecting users’ data. In the second stage, both doctors and patients were generally interested in using hypertension apps, but most had no experience with these apps due to a lack of awareness of their availability and suitability. Patients and doctors liked apps that combine intuitive interfaces with a pleasant and clear visual design, in-depth features (eg, color-coded feedback accompanied with textual explanations), activity-specific reminders, and educational content regarding hypertension and potential complications. When the pros and cons of the 5 apps were discussed, 3 apps were identified as being more suitable, with Cora Health rated the highest by the participants. Conclusions Only 5 apps were deemed potentially effective and secure. Patients’ and doctors’ discussions of the pros and cons of these 5 apps revealed that 3 out of the 5 are clearly more suitable, with the Cora Health app being judged most suitable overall.

Socializing Sensorimotor Contingencies

The aim of this review is to highlight the idea of grounding social cognition in sensorimotor interactions shared across agents. We discuss an action-oriented account that emerges from a broader interpretation of the concept of sensorimotor contingencies. We suggest that dynamic informational and sensorimotor coupling across agents can mediate the deployment of action-effect contingencies in social contexts. We propose this concept of socializing sensorimotor contingencies (socSMCs) as a shared framework of analysis for processes within and across brains and bodies, and their physical and social environments. In doing so, we integrate insights from different fields, including neuroscience, psychology, and research on human–robot interaction. We review studies on dynamic embodied interaction and highlight empirical findings that suggest an important role of sensorimotor and informational entrainment in social contexts. Furthermore, we discuss links to closely related concepts, such as enactivism, models of coordination dynamics and others, and clarify differences to approaches that focus on mentalizing and high-level cognitive representations. Moreover, we consider conceptual implications of rethinking cognition as social sensorimotor coupling. The insight that social cognitive phenomena like joint attention, mutual trust or empathy rely heavily on the informational and sensorimotor coupling between agents may provide novel remedies for people with disturbed social cognition and for situations of disturbed social interaction. Furthermore, our proposal has potential applications in the field of human–robot interaction where socSMCs principles might lead to more natural and intuitive interfaces for human users.

Review of Capacitive Touchscreen Technologies: Overview, Research Trends, and Machine Learning Approaches

Touchscreens have been studied and developed for a long time to provide user-friendly and intuitive interfaces on displays. This paper describes the touchscreen technologies in four categories of resistive, capacitive, acoustic wave, and optical methods. Then, it addresses the main studies of SNR improvement and stylus support on the capacitive touchscreens that have been widely adopted in most consumer electronics such as smartphones, tablet PCs, and notebook PCs. In addition, the machine learning approaches for capacitive touchscreens are explained in four applications of user identification/authentication, gesture detection, accuracy improvement, and input discrimination.

Identification of the Most Suitable App to Support the Self-Management of Hypertension: Systematic Selection Approach and Qualitative Study (Preprint)

BACKGROUND Smartphone apps are increasingly being used to aid in hypertension self-management, and a large and ever-growing number of self-management apps have been commercially released. However, very few of these are potentially effective and secure, and researchers have yet to establish the suitability of specific hypertension apps to particular contexts. OBJECTIVE The aim of this study is to identify the most suitable hypertension app in the context of Saudi Arabia and its health system. METHODS This study used a 2-stage approach to selecting the most suitable app for hypertension self-management. First, a systematic selection approach was followed to identify a shortlist of the most suitable apps according to the criteria of potential effectiveness, theoretical underpinning, and privacy and security. Second, an exploratory qualitative study was conducted to select the most suitable from the shortlist: 12 doctors were interviewed, and 22 patients participated in 4 focus groups. These explored participants’ attitudes towards self-management apps in general, and their views towards the apps identified via the systematic selection process. The qualitative data were analyzed using framework analysis. RESULTS In the first stage, only 5 apps were found to be potentially effective while also having a theoretical underpinning and protecting users’ data. In the second stage, both doctors and patients were generally interested in using hypertension apps, but most had no experience with these apps due to a lack of awareness of their availability and suitability. Patients and doctors liked apps that combine intuitive interfaces with a pleasant and clear visual design, in-depth features (eg, color-coded feedback accompanied with textual explanations), activity-specific reminders, and educational content regarding hypertension and potential complications. When the pros and cons of the 5 apps were discussed, 3 apps were identified as being more suitable, with Cora Health rated the highest by the participants. CONCLUSIONS Only 5 apps were deemed potentially effective and secure. Patients’ and doctors’ discussions of the pros and cons of these 5 apps revealed that 3 out of the 5 are clearly more suitable, with the Cora Health app being judged most suitable overall.

LeafLive-DB: Classification and Data Storage of Botanical Studies

The development of studies, projects, and technologies that contribute to the understanding and preservation of plant biodiversity is becoming highly necessary, as well as tools and software platforms that enable the storage and classification of information resulting from studies on biodiversity. This work presents LeafLive-DB, a software platform that helps map and characterize species from the Brazilian plant biodiversity, offering the possibility of worldwide distribution. Developed by Brazilian and Peruvians researchers, this platform, which is available in its first version, features some functions for consulting and registering plant species and their taxonomy, among other information, through intuitive interfaces and an environment that promotes collaboration and data and research sharing. The platform innovates in data processing, functionality, and development architecture. It has ten thousand registers, and it should start to be distributed in partnership with schools and higher education institutions.

Visual analytics of sensor movement data for cheetah behaviour analysis

Current tracking technology such as GPS data loggers allows biologists to remotely collect large amounts of movement data for a large variety of species. Extending, and often replacing interpretation based on observation, the analysis of the collected data supports research on animal behaviour, on impact factors such as climate change and human intervention on the globe, as well as on conservation programs. However, this analysis is difficult, due to the nature of the research questions and the complexity of the data sets. It requires both automated analysis, for example, for the detection of behavioural patterns, and human inspection, for example, for interpretation, inclusion of previous knowledge, and for conclusions on future actions and decision making. For this analysis and inspection, the movement data needs to be put into the context of environmental data, which helps to interpret the behaviour. Thus, a major challenge is to design and develop methods and intuitive interfaces that integrate the data for analysis by biologists. We present a concept and implementation for the visual analysis of cheetah movement data in a web-based fashion that allows usage both in the field and in office environments. Graphic abstract

Outcomes assessment pitfalls: challenges to quantifying knowledge gain in a sex education game

Background: The use of videogames as a public health tool is rapidly expanding. Accurate assessment of the efficacy of such games is complicated by many factors. We describe challenges associated with measuring the impact of playing a videogame with information about human sexual anatomy and reproduction and discuss motivations for, and solutions to, these challenges. Methods: The My Future Family Game (MFF) is a validated tool for collecting data about family planning intentions which includes information about human anatomy and sexual reproduction. We sought to assess the efficacy of the game as a tool for teaching sexual education using a pre-post model which was deployed in three schools in and around Chennai, India in summer of 2018. Results: The MFF game was successfully modified to collect data about players’ pre-gameplay knowledge of sexual anatomy and processes. The post gameplay assessment process we used did not effectively assess knowledge gain. Designing assessments for games dealing with sexuality presents challenges including: effectively communicating about biological parts and processes, designing usable and intuitive interfaces with minimal text, ensuring that all parts of the process are fun, and integrating assessments into the game in a way that makes them invisible. Conclusion: Games can be an effective means of gathering data about knowledge of sex and reproduction that it is difficult to obtain through other means. Assessing knowledge about human sexual reproduction is complicated by cultural norms and taboos, and technical hurdles which can be addressed through careful design. This study adds to the sparse literature in the field by providing information about pitfalls to avoid and best practices in this evolving area.

Mobile Applications, Geolocation and Information Technologies for the Study and Communication of the Heritage Value of Public Works

Public works’ relationship with the landscape should be among the factors addressed in heritage studies. Their association with place defines their purpose while contributing to the construction of the cultural landscape. Unawareness of the existence of public works is their most powerful enemy. Inventorying and cataloguing are therefore imperative in any study of these assets. The research described here drew from information technologies to develop cost-free mobile apps in an innovative approach to dissemination with the aim to enhance public awareness of and esteem for such elements. The applications described, which are all author-developed and designed to promote Spain’s heritage public works, address the heritage value of rural public works (VAPROP_Rutas), geolocation of the built heritage (GEOPACK), heritage public works on the River Duero (PHDuero), major works of engineering (GOING) and geolocation of Eduardo Torroja’s public works (underway). All these applications build on the geo-positioning features of mobile handsets and are characterised by simple and intuitive interfaces for ready accessibility by the public at large. Users may participate in the experience by entering useful information, new content and suggestions. These apps deploy the latest technologies to enhance the understanding and appreciation of civil works by explaining their territorial, social and cultural significance.