Smart Health最新文献

{"title":"Ultra low-power, wearable, accelerated shallow-learning fall detection for elderly at-risk persons","authors":"Jingxiao Tian ,&nbsp;Patrick Mercier ,&nbsp;Christopher Paolini","doi":"10.1016/j.smhl.2024.100498","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100498","url":null,"abstract":"<div><p>This work focuses on the development and manufacturing of a wireless, wearable, low-power fall detection sensor (FDS) designed to predict and detect falls in elderly at-risk individuals. Unintentional falls are a significant risk in this demographic, often resulting from diminished physical capabilities such as reduced hand grip strength and complications from conditions like arthritis, vertigo, and neuromuscular issues. To address this, we utilize advanced low-power field-programmable gate arrays (FPGAs) to implement a fixed-function neural network capable of categorizing activities of daily life (ADLs), including the detection of falls. This system employs a Convolutional Neural Network (CNN) model, trained and validated using the Caffe deep learning framework with data collected from human subjects experiments. This system integrates an ST Microelectronics LSM6DSOX inertial measurement unit (IMU) sensor, embedded with an ultra-low-power Lattice iCE40UP FPGA, which samples and stores joint acceleration and orientation rate. Additionally, we have acquired and published a dataset of 3D accelerometer and gyroscope measurements from predefined ADLs and falls, using volunteer human subjects. This innovative approach aims to enhance the safety and well-being of older adults by providing timely and accurate fall detection and prediction.</p><p>In this paper, we present an innovative approach to utilizing a compact Convolutional Neural Network (CNN) core for accelerating convolutional operations on a machine learning model, suitable for deployment on an ultra-low power FPGA.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"33 ","pages":"Article 100498"},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352648324000540/pdfft?md5=436ed873a93f5155977c1d04d19122da&pid=1-s2.0-S2352648324000540-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141297939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital native students using nutritional apps: are they more adherent to a mediterranean diet model? Results from the good APPetite survey","authors":"Silvia Marconi,&nbsp;Elisa Carrara,&nbsp;Giulia Gilberti,&nbsp;Maurizio Castellano,&nbsp;Barbara Zanini","doi":"10.1016/j.smhl.2024.100497","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100497","url":null,"abstract":"<div><p>Reading and understanding food labels are crucial steps in healthy dietary choices. Nutrition-related applications (n-apps) have increased in the recent years and the aim of this study was to assess the use and the perception of n-apps among a population of university students, also investigating the attitude and relationship with reading food labels and adherence to the Mediterranean diet (Medi-Lite score).</p><p>In 2023, 316 students, mainly attending the courses of Medicine, Pharmacy and Dietetics at the University of Brescia, Italy, completed an anonymous and specifically designed survey. 33.9% of the students stated that they use or have used n-apps. The most used apps were Yuka, MyFitnessPal, Fat Secret and Yazio, especially for the ease of use, speed, nutritional values estimation and barcode reading. 53.2% and 53.5% of the students declared to be food information and nutrition label readers respectively and the Medi-Lite mean value was 9.98 ± 2.46. N-app-users were significantly more attentive to food information and nutrition label than app not-users (both p &lt; 0.0001) and recorded a Medi-Lite score significantly higher (p = 0.0131).</p><p>The present study found for the first time an extensive correlation between the use of n-apps, the food labels awareness and healthy eating habits in a digitally native population.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"33 ","pages":"Article 100497"},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141291547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The impact of an AI-driven personal health platform on cardiovascular disease risk","authors":"Craig McNulty ,&nbsp;Justin Holland ,&nbsp;Cameron McDonald ,&nbsp;Marshall J. McGee","doi":"10.1016/j.smhl.2024.100499","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100499","url":null,"abstract":"<div><p>Cardiovascular disease (CVD) is a global health concern, particularly among older adults, linked to fat and sugar-rich diet, inactivity, tobacco smoking, as well as genetic factors. Regular exercise and a balanced diet are recommended to reduce CVD risk. Recently, the use of computer of mobile phone application platforms have become promenent tools for personal health interventions and guidance. Although research findings of the efficacy of mobile health apps has had mixed results, improved user health and lowered risk of CVD has been seen with regular engagement. Shae, an AI-driven mobile health platform, offers personalized lifestyle guidance based on an individual's phenotype. The aim of this study is to assess whether engagement with the online platform, Shae, decreases Framingham non-laboratory CVD risk score over time.</p><p>The study collected data from 1684 participants who engaged with the Shae online platform for at least 3-months between May 2014 and March 2018. After applying exclusion criteria, 5225 complete assessment entries remained for analysis. The Shae platform assessed health, predicted future risks, and recommended personalized interventions. Data included anthropometric measurements and survey responses, converted for standardized analysis. Descriptive statistics were calculated for participants' characteristics and compared across gender and age groups, in alignment with established cardiovascular disease (CVD) risk factors. A linear mixed-effect regression model was employed to assess changes in CVD risk over time. This research aimed to evaluate the impact of personalized health interventions on CVD risk in diverse demographic groups.</p><p>CVD risk score, when adjusted for age, decreased by 11.2% following 24 months of engagement with the Shae app, with men 45 years and older seeing the largest decrease (16.1%), and the lowest decrease seen in women under 55 years (10%). Use of the Shae app was primarily by women (85%).</p><p>This study assessed the impact of engagement with the mHealth app, Shae, on reducing CVD risk. Results revealed a significant CVD risk reduction, notably in men over 45. mHealth apps like Shae provide personalized health assessments, activity tracking, nutrition advice, and medication reminders, enabling proactive cardiovascular health management. Increased physical activity and better blood pressure control were key factors in risk reduction. Tailored interventions for high-risk groups align with previous research. Given the importance of preventing CVD, mHealth apps hold promise. This 24-month study offers robust insights, and the focus on individual phenotypes enhances engagement and health outcomes.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"33 ","pages":"Article 100499"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352648324000552/pdfft?md5=f00ef3c52ea6e6893fd73aa926a9eafe&pid=1-s2.0-S2352648324000552-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141244888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"APC: Contactless healthy sitting posture monitoring with microphone array","authors":"Kaiyuan Ma ,&nbsp;Shunan Song ,&nbsp;Lingling An ,&nbsp;Shiwen Mao ,&nbsp;Xuyu Wang","doi":"10.1016/j.smhl.2024.100463","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100463","url":null,"abstract":"<div><p>The prevalence of poor sitting posture in daily work has become a growing concern among office workers and students due to the associated health problems. To address this issue, we design an acoustic sitting posture care system (termed, APC) based on a circular microphone array. Compared with classic posture recognition technologies such as visual perception and sensors, acoustic sensing naturally possesses advantages such as privacy protection and contactless capabilities. Concretely, our system leverages a customized and inaudible sound signal sent from a speaker to a user’s body, and an echo signal preprocessing method to sense the body posture. Our system comprises three modules: signal generation and collection, signal preprocessing, and posture classification. The signal generation and collection module is designed to create an appropriate signal waveform for transmitting the sound signal. We also develop a unique alignment method for received signals to implement background interference cancellation. In the signal preprocessing module, we propose a body profile extraction method based on the phase difference between received signals. In the posture classification module, we design an attention mechanism based classification network that can map the output of the previous module to different sitting posture categories. The experimental results show that our proposed method achieves an average accuracy of 98.4% for five common sitting postures. Furthermore, case studies conducted under different practical conditions have validated the robustness of our system.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"32 ","pages":"Article 100463"},"PeriodicalIF":0.0,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140638110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adapting goals and motivational messages on smartphones for motivation to walk","authors":"David Rei,&nbsp;Céline Clavel,&nbsp;Jean-Claude Martin,&nbsp;Brian Ravenet","doi":"10.1016/j.smhl.2024.100482","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100482","url":null,"abstract":"<div><p>Physical activity is one of the most recognised means of disease prevention. While several studies investigated different techniques for motivating people to walk (e.g. adaptive <em>goal setting</em>), only few studies considered user profiles to personalise interactions. In this article, we propose a new interaction model which adapts walking goals and motivational messages in a static way (using the initial physical activity level) and in a dynamic way (using previous days’ performance). We explain how we implemented this model in a mobile application counting and displaying in real time the number of steps, an adapted daily goal and personalised motivational messages throughout the day. We describe two field studies conducted with 32 and 50 users over four weeks. We compare the impacts of adapted daily goals and personalised motivational messages on users’ step counts and motivation to walk. Participants using the adapted version of the mobile application displayed an increase in their motivation to walk after the intervention and were more physically active than participants using non adapted versions of the mobile application.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"32 ","pages":"Article 100482"},"PeriodicalIF":0.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140638111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A bio-inspired model for robust navigation assistive devices","authors":"Simon L. Gay ,&nbsp;Edwige Pissaloux ,&nbsp;Jean-Paul Jamont","doi":"10.1016/j.smhl.2024.100484","DOIUrl":"10.1016/j.smhl.2024.100484","url":null,"abstract":"<div><p>This paper proposes a new implementation and evaluation in a real-world environment of a bio-inspired predictive navigation model for mobility control, suitable especially for assistance of visually impaired people and autonomous mobile systems. This bio-inspired model relies on the interactions between formal models of three types of neurons identified in the mammals’ brain implied in navigation tasks, namely place cells, grid cells, and head direction cells, to construct a topological model of the environment under the form of a decentralized navigation graph. Previously tested in virtual environments, this model demonstrated a high tolerance to motion drift, making possible to map large environments without the need to correct it to handle such drifts, and robustness to environment changes. The presented implementation is based on a stereoscopic camera, and is evaluated on its possibilities to map and guide a person or an autonomous mobile robot in an unknown real environment. The evaluation results confirm the effectiveness of the proposed bio-inspired navigation model to build a path map, localize and guide a person through this path. The model predictions remain robust to environment changes, and allow to estimate traveled distances with an error rate below 3% over test paths, up to 100m. The tests performed on a robotic platform also demonstrated the pertinence of navigation data produced by this navigation model to guide an autonomous system. These results open the way toward efficient wearable assistive devices for visually impaired people independent navigation.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"33 ","pages":"Article 100484"},"PeriodicalIF":0.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352648324000400/pdfft?md5=da26293fcfb66b6f6d83277cab8ac5b3&pid=1-s2.0-S2352648324000400-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140789352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A smart demonstration unit for upper-limb myoelectric prostheses","authors":"Damiano Cosma Potenza ,&nbsp;Andrea Grazioso ,&nbsp;Federico Gaetani ,&nbsp;Giacomo Mantriota ,&nbsp;Giulio Reina","doi":"10.1016/j.smhl.2024.100481","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100481","url":null,"abstract":"<div><p>Demonstration unit devices are designed and built for the CPO (Certified Prosthetist Orthotist) to illustrate the functionalities of robotic hands to patients without the need for a prosthetic socket. Demonstration units are also of invaluable importance during the calibration and learning stages, which are preparatory for the final adoption of a prosthesis. This paper presents a smart demo unit (SDU) developed for Adam’s Hand, a robotic underactuated prosthesis proposed by BionIT Labs. It serves as a prosthetic sleeve that powers the robotic hand and gathers the electromyographic commands from the user. The details of the SDU are presented in terms of hardware and firmware, devoting special attention to the electromechanical interface with the robotic hand and the patient or CPO. The advantages of the system over existing alternatives are discussed, showing that the proposed SDU can be a valuable tool in the field of extremity upper limb prosthetics.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"32 ","pages":"Article 100481"},"PeriodicalIF":0.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140638275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Secondary care for subjects with stroke: Compliance, usability and technological acceptance of the vCare platform solution","authors":"Agnese Seregni ,&nbsp;Peppino Tropea ,&nbsp;Riccardo Re ,&nbsp;Verena Biscaro ,&nbsp;Elda Judica ,&nbsp;Massimo Caprino ,&nbsp;Kai Gand ,&nbsp;Hannes Schlieter ,&nbsp;Massimo Corbo","doi":"10.1016/j.smhl.2024.100483","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100483","url":null,"abstract":"<div><p>The continuity of care of subjects with chronic Non-Communicable Diseases (NCDs) is a well-known public health problem. To address this issue, various home-based-technological solutions have been proposed to provide personalized home rehabilitation plans: however, enhancing the compliance is still a challenge. In the framework of the vCare project, an innovative technological home-based platform was developed to provide care and rehabilitation services (motor and cognitive training, e-learning service, and recommendations for additional activities) within a coaching environment in a real-life scenario.</p><p>The aim of this work was to evaluate the compliance of post stroke subjects with the solution, and the platform's usability and technological acceptance.</p><p>Patients with stroke underwent the personalized home rehabilitation plan for up to 9 weeks. Clinical status and quality of life were assessed before and after the experimental period; compliance, usability and technological acceptance at the end.</p><p>Patients experienced the vCare solution without adverse events following their clinical plan. Results were suitable: motor and cognitive training reached 66% and 95% of adherence, respectively. Usability and technological acceptance were above the limits of acceptability.</p><p>The vCare coaching system might potentially motivate and empower patients with functional disabilities to actively engage themselves in carrying out, autonomously, personalized rehabilitation activities at home.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"32 ","pages":"Article 100483"},"PeriodicalIF":0.0,"publicationDate":"2024-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140643625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Illuminating precise stencils on surgical sites using projection-based augmented reality","authors":"Muhammad Twaha Ibrahim ,&nbsp;Aditi Majumder ,&nbsp;M. Gopi ,&nbsp;Lohrasb R. Sayadi ,&nbsp;Raj M. Vyas","doi":"10.1016/j.smhl.2024.100476","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100476","url":null,"abstract":"<div><p>In this paper we propose a system that connects surgeons to remote or local experts who provide real-time surgical guidance by illuminating salient markings or stencils (e.g. points, lines and curves) on the physical surgical site using a projector. The projection can be modified in real time by the expert using a GUI and can be seen by all in the operating room (OR) without the use of any wearables. This system overcomes the limitations of AR/VR headsets which can overlay information through a headset, but are obtrusive, not very accurate with movements, and visible only to the surgeon excluding others in the room. Overlaying information, at high precision, directly on the physical surgical site that can be seen by everyone in the OR can become an useful tool for skill transfer, expert consultation and training, especially in telemedicine.</p><p>In addition to the projector, the system comprises of a RGB-D camera (e.g. Kinect) for feedback, together designated as the PDC (<u>P</u>rojector <u>D</u>epth <u>C</u>amera) unit. The PDC is driven by a PC. The RGB-D camera provides depth information in addition to an image at video frame rates. A high resolution mesh of the surgical site is captured using the PDC unit initially. During the surgical planning, training or execution session, this digital model can be marked by appropriate incision markings on a tablet or monitor using touch based or mouse based interface, on the same local machine or after being transmitted to a remote machine. These markings are then communicated back to the PDC unit and illuminated at high precision via the projector on the surgical site in real time. If the surgical site moves during the process, the movement is tracked and updated quickly on the surgical site. Our method specifically overcomes the obtrusive, exclusive, and indirect attributes of headsets and displays while maintaining high accuracy of registration with movements.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"32 ","pages":"Article 100476"},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352648324000321/pdfft?md5=a5b29d483e4f78a016c0c96ecde0304f&pid=1-s2.0-S2352648324000321-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140539512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding reciprocity in human–robot interactions through completion of a pregiving favor","authors":"Reilly Moberg,&nbsp;Edward Downs,&nbsp;Abby Shelby,&nbsp;Arshia Khan","doi":"10.1016/j.smhl.2024.100466","DOIUrl":"https://doi.org/10.1016/j.smhl.2024.100466","url":null,"abstract":"<div><p>In today’s world, understanding how people interact with humanoid social robots is important for day-to-day interactions and design purposes. A phasic, between-subjects, psychophysiological experiment (N <span><math><mo>=</mo></math></span> 72) examined how the norm of reciprocity influenced interactions with the humanoid social robot, “Pepper”. Facial electromyography (zygomatic and corrugator) was measured to determine participant’s emotional valence during interaction. The level of reciprocity in response to a pregiving favor was measured by the number of raffle tickets purchased by participants at the robot’s request. Results suggest that the social rule of reciprocation exists within human–robot interaction. When Pepper offered a pregiving favor to a participant, that person was more likely to reciprocate via the robot’s later ticket purchase request. Contributions to theory and design of humanoid social robots are discussed, as well as avenues for future research.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"32 ","pages":"Article 100466"},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140535706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}