Human Health Engineering Volume II

In this Special Issue on “Human Health Engineering Volume II”, we invited submissions exploring recent contributions to the field of human health engineering, i.e., technology for monitoring the physical or mental health status of individuals in a variety of applications. Contributions could focus on sensors, wearable hardware, algorithms, or integrated monitoring systems. We organized the different papers according to their contributions to the main parts of the monitoring and control engineering scheme applied to human health applications, namely papers focusing on measuring/sensing physiological variables, papers highlighting health-monitoring applications, and examples of control and process management applications for human health. In comparison to biomedical engineering, we envision that the field of human health engineering will also cover applications for healthy humans (e.g., sports, sleep, and stress), and thus not only contribute to the development of technology for curing patients or supporting chronically ill people, but also to more general disease prevention and optimization of human well-being

How a Diverse Research Ecosystem Has Generated New Rehabilitation Technologies: Review of NIDILRR’s Rehabilitation Engineering Research Centers

Over 50 million United States citizens (1 in 6 people in the US) have a developmental, acquired, or degenerative disability. The average US citizen can expect to live 20% of his or her life with a disability. Rehabilitation technologies play a major role in improving the quality of life for people with a disability, yet widespread and highly challenging needs remain. Within the US, a major effort aimed at the creation and evaluation of rehabilitation technology has been the Rehabilitation Engineering Research Centers (RERCs) sponsored by the National Institute on Disability, Independent Living, and Rehabilitation Research. As envisioned at their conception by a panel of the National Academy of Science in 1970, these centers were intended to take a “total approach to rehabilitation”, combining medicine, engineering, and related science, to improve the quality of life of individuals with a disability. Here, we review the scope, achievements, and ongoing projects of an unbiased sample of 19 currently active or recently terminated RERCs. Specifically, for each center, we briefly explain the needs it targets, summarize key historical advances, identify emerging innovations, and consider future directions. Our assessment from this review is that the RERC program indeed involves a multidisciplinary approach, with 36 professional fields involved, although 70% of research and development staff are in engineering fields, 23% in clinical fields, and only 7% in basic science fields; significantly, 11% of the professional staff have a disability related to their research. We observe that the RERC program has substantially diversified the scope of its work since the 1970’s, addressing more types of disabilities using more technologies, and, in particular, often now focusing on information technologies. RERC work also now often views users as integrated into an interdependent society through technologies that both people with and without disabilities co-use (such as the internet, wireless communication, and architecture). In addition, RERC research has evolved to view users as able at improving outcomes through learning, exercise, and plasticity (rather than being static), which can be optimally timed. We provide examples of rehabilitation technology innovation produced by the RERCs that illustrate this increasingly diversifying scope and evolving perspective. We conclude by discussing growth opportunities and possible future directions of the RERC program

Hybrid Wearable Signal Processing/Learning via Deep Neural Networks

Wearable technologies are gaining considerable attention in recent years as a potential post-smartphone platform with several applications of significant engineering importance. Wearable technologies are expected to become more prevalent in a variety of areas, including modern healthcare practices, robotic prosthesis control, Artificial Reality (AR) and Virtual Reality (VR) applications, Human Machine Interface/Interaction (HMI), and remote support for patients and chronically ill patients at home. The emergence of wearable technologies can be attributed to the advancement of flexible electronic materials; the availability of advanced cloud and wireless communication systems, and; the Internet of Things (IoT) coupled with high demand from the tech-savvy population and the elderly population for healthcare management. Wearable devices in the healthcare realm gather various biological signals from the human body, among which Electrocardiogram (ECG), Photoplethysmogram (PPG), and surface Electromyogram (sEMG), are the most widely non-intrusive monitored signals. Utilizing these widely used non-intrusive signals, the primary emphasis of the proposed dissertation is on the development of advanced Machine Learning (ML), in particular Deep Learning (DL), algorithms to increase the accuracy of wearable devices in specific tasks. In this context and in the first part, using ECG and PPG bio-signals, we focus on development of accurate subject-specific solutions for continuous and cuff-less Blood Pressure (BP) monitoring. More precisely, a deep learning-based framework known as BP-Net is proposed for predicting continuous upper and lower bounds of blood pressure, respectively, known as Systolic BP (SBP) and Diastolic BP (DBP). Furthermore, by capitalizing on the fact that datasets used in recent literature are not unified and properly defined, a unified dataset is constructed from the MIMIC-I and MIMIC-III databases obtained from PhysioNet. In the second part, we focus on hand gesture recognition utilizing sEMG signals, which have the potential to be used in the myoelectric prostheses control systems or decoding Myo Armbands data to interpret human intent in AR/VR environments. Capitalizing on the recent advances in hybrid architectures and Transformers in different applications, we aim to enhance the accuracy of sEMG-based hand gesture recognition by introducing a hybrid architecture based on Transformers, referred to as the Transformer for Hand Gesture Recognition (TraHGR). In particular, the TraHGR architecture consists of two parallel paths followed by a linear layer that acts as a fusion center to integrate the advantage of each module. The ultimate goal of this work is to increase the accuracy of gesture classifications, which could be a major step towards the development of more advanced HMI systems that can improve the quality of life for people with disabilities or enhance the user experience in AR/VR applications. Besides improving accuracy, decreasing the number of parameters in the Deep Neural Network (DNN) architectures plays an important role in wearable devices. In other words, to achieve the highest possible accuracy, complicated and heavy-weighted Deep Neural Networks (DNNs) are typically developed, which restricts their practical application in low-power and resource-constrained wearable systems. Therefore, in our next attempt, we propose a lightweight hybrid architecture based on the Convolutional Neural Network (CNN) and attention mechanism, referred to as Hierarchical Depth-wise Convolution along with the Attention Mechanism (HDCAM), to effectively extract local and global representations of the input. The key objective behind the design of HDCAM was to ensure its resource efficiency while maintaining comparable or better performance than the current state-of-the-art methods

Dados epidemiológicos nacionais de amputação e proposta de dispositivo para treinamento de usuários de próteses de membro superior

Amputation rates in a country can be related to government policies, the prevalence of certain diseases and the educational level of the studied population. Brazil has the sixth largest population in the world and the current scarcity of statistical studies on amputation rates in the country may delay the insertion of technologies that improve techniques during motor rehabilitation. Thus, a survey of the national academic literature was carried out and compared with the reality of the amputation rates found. Since 2011, the Northeast region has had the highest incidence in total amputations, lower limbs and upper limbs. In 2019, the states of Alagoas and Sergipe had 27 amputations per 100,000 inhabitants and the literature of this region, in 20 years, is totally focused on the health area. The region with the largest number of publications in application of engineering in the field of motor rehabilitation in amputees is the Southeast region, totaling 7 articles in scientific journals. This fact shows that the technology is still little inserted in this area. The pre-prosthetic phase is one of the most important phases in rehabilitation because the longer the interval from the post-surgical phase to the beginning of rehabilitation, the less the potential to recover motor skills lost with amputation. Thus, the insertion of technology in this area can bring numerous benefits. Currently, there are active prostheses with myoelectric control that allow advances in the execution of increasingly mimetic movements with emphasis on healthy human members, but which unfortunately have a high acquisition cost. In this work, a proposal for a portable armband device was developed through the collection of detailed electromyographic information of the remaining muscles of the stump and spatial limb tracking, which allows integration with software, virtual prosthesis, serious games, beginning the rehabilitation, carrying the benefits of motor training from myoelectric control without the need for physical prosthesis.CNPq - Conselho Nacional de Desenvolvimento Científico e TecnológicoFAPEMIG - Fundação de Amparo a Pesquisa do Estado de Minas GeraisTrabalho de Conclusão de Curso (Graduação)As taxas de amputações em um país podem estar relacionadas às políticas públicas de um governo, a predominância de certas doenças e ao nível educacional da população estudada. O Brasil possui a sexta maior população mundial e a presente escassez de estudos estatísticos em taxas de amputações no país pode atrasar a inserção de tecnologias que aprimorem as técnicas durante a reabilitação motora. Assim, foi realizado um levantamento da literatura acadêmica nacional e comparado com as taxas de amputações encontradas. A região Nordeste apresentou desde 2011, a maior incidência no total de amputações, de MMII e MMSS. Em 2019, os estados Alagoas e Sergipe apresentaram 27 amputações por 100.000 habitantes e a literatura desta região, em 20 anos, é totalmente voltada à área da saúde. A região com a maior quantidade de publicações em aplicação da engenharia na área de reabilitação motora em amputados é a região Sudeste, totalizando 7 artigos em revistas científicas. Tal fato apresenta que a tecnologia ainda está pouco inserida nesta área. A fase pré-protética é uma das fases mais importantes na reabilitação pois quanto maior o intervalo da fase pós-cirúrgica até o início da reabilitação, menor será o potencial em recuperar as capacidades motoras perdidas com a amputação. Assim, inserção da tecnologia nesta área pode trazer inúmeros benefícios. Atualmente existem as próteses ativas com controle mioelétrico que permitem avanços na execução dos movimentos cada vez mais miméticos com relação aos membros humanos saudáveis, mas que infelizmente apresentam um alto custo de aquisição. Neste trabalho foi desenvolvido uma proposta de dispositivo armband portátil através da coleta das informações eletromiográficas detalhadas dos músculos remanescentes do coto e do rastreamento espacial do membro, que permita a integração com softwares, prótese virtual, jogos sérios dando início à reabilitação, carregando os benefícios do treinamento motor a partir do controle mioelétrico sem a necessidade da prótese física