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Latent Space Representation of Adversarial AutoEncoder for Human Activity Recognition: Application to a low-cost commercial force plate and inertial measurement units 用于人体活动识别的对抗性自编码器的潜在空间表示：在低成本商用测力板和惯性测量单元上的应用

Smart Health Pub Date : 2025-03-01 Epub Date: 2025-01-04 DOI: 10.1016/j.smhl.2024.100537

Kenta Kamikokuryo , Gentiane Venture , Vincent Hernandez

{"title":"Latent Space Representation of Adversarial AutoEncoder for Human Activity Recognition: Application to a low-cost commercial force plate and inertial measurement units","authors":"Kenta Kamikokuryo , Gentiane Venture , Vincent Hernandez","doi":"10.1016/j.smhl.2024.100537","DOIUrl":"10.1016/j.smhl.2024.100537","url":null,"abstract":"<div><div>Human Activity Recognition (HAR) is a key component of a home rehabilitation system that provides real-time monitoring and personalized feedback. This research explores the application of Adversarial AutoEncoder (AAE) models for data dimensionality reduction in the context of HAR. Visualizing data in a lower-dimensional space is important to understand changes in motor control due to medical conditions or aging, to aid personalized interventions, and to ensure continuous benefits in remote rehabilitation settings. This makes patient assessment effective, easier, and faster.</div><div>In this study, the classification performance of the latent space created by the AAE is evaluated using the Wii Balance Board (WiiBB) and/or three Inertial Measurement Units (IMUs) placed on the forearms and hip. Various sensor configurations are considered, including only WiiBB, only IMUs, combinations of WiiBB with the IMU at the hip, and combinations of WiiBB with the 3 IMUs.</div><div>The accuracy of the latent space representation is compared with two common supervised classification models, which are the Convolutional Neural Network (CNN) and the neural network called CNNLSTM, which is composed of convolution layers followed by recurrent layers. The approach was demonstrated for two different sets of exercises consisting of upper and lower body exercises collected with 19 participants.</div><div>The results show that the latent space representation of the AAE achieves a strong classification accuracy performance while also serving as a visualization tool. This study is an initial demonstration of the potential of integrating WiiBB and IMU sensors for comprehensive activity recognition for upper and lower body movement analysis.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"35 ","pages":"Article 100537"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143129614","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}

引用次数: 0

Data-driven assessment of the effectiveness of non-pharmaceutical interventions on Covid spread mitigation in Italy 数据驱动评估意大利非药物干预措施对缓解Covid - 19传播的有效性

Smart Health Pub Date : 2025-03-01 Epub Date: 2024-12-05 DOI: 10.1016/j.smhl.2024.100524

Divya Pragna Mulla , Mario Alessandro Bochicchio , Antonella Longo

{"title":"Data-driven assessment of the effectiveness of non-pharmaceutical interventions on Covid spread mitigation in Italy","authors":"Divya Pragna Mulla , Mario Alessandro Bochicchio , Antonella Longo","doi":"10.1016/j.smhl.2024.100524","DOIUrl":"10.1016/j.smhl.2024.100524","url":null,"abstract":"<div><div>To mitigate the impact of pandemics such as COVID-19, governments can implement various Non-Pharmaceutical Interventions (NPIs), ranging from the use of personal protective equipment to social distancing measures. While it has been demonstrated that NPIs can be effective over time, the assessment of their efficacy and the estimation of their cost-benefit ratio are still debated issues. For COVID-19, several authors have used case confirmation as a key parameter to assess the efficacy of NPIs. In this paper, we compare the efficacy of this parameter to that of the death rate, hospitalizations, and intensive care unit cases, in conjunction with human mobility indicators, in evaluating the effectiveness of NPIs. Our research uses data on daily COVID-19 cases and deaths, intensive care unit cases, hospitalizations, Google Mobility Reports, and NPI data from all Italian regions from March 2020 to May 2022. The evaluation method is based on the approach proposed by Wang et al., in 2020 to assess the impact of NPI efficacy and understand the effect of other parameters. Our results indicate that, when combined with human mobility indicators, the mortality rate and the number of intensive care units perform better than the number of cases in determining the efficacy of NPIs. These findings can assist policymakers in developing the best data-driven methods for dealing with confinement problems and planning for future outbreaks.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"35 ","pages":"Article 100524"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143129220","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}

引用次数: 0

SAFE: Sound Analysis for Fall Event detection using machine learning SAFE：使用机器学习进行坠落事件检测的可靠分析

Smart Health Pub Date : 2025-03-01 Epub Date: 2025-01-06 DOI: 10.1016/j.smhl.2024.100539

Antony Garcia , Xinming Huang

引用次数: 0

A novel convolutional interpretability model for pixel-level interpretation of medical image classification through fusion of machine learning and fuzzy logic 一种融合机器学习和模糊逻辑的医学图像分类像素级解释卷积可解释性模型

Smart Health Pub Date : 2025-03-01 Epub Date: 2024-12-21 DOI: 10.1016/j.smhl.2024.100535

Mohammad Ennab, Hamid Mcheick

{"title":"A novel convolutional interpretability model for pixel-level interpretation of medical image classification through fusion of machine learning and fuzzy logic","authors":"Mohammad Ennab, Hamid Mcheick","doi":"10.1016/j.smhl.2024.100535","DOIUrl":"10.1016/j.smhl.2024.100535","url":null,"abstract":"<div><div>Artificial intelligence (AI) models for medical image analysis have achieved high diagnostic performance, but they often lack interpretability, limiting their clinical adoption. Existing methods can explain predictions at the image level, but they cannot provide pixel-level insights. This study proposes a novel fusion of machine learning and fuzzy logic to develop an interpretable model that can precisely identify discriminative image regions driving diagnostic decisions and generate heatmap visualization. The model is trained and evaluated on a dataset of CT scans containing healthy and diseased organ images. Quantitative features are extracted across pixels and normalized into representation matrices using a machine learning model. Subsequently, the contribution of each detected lesion to the overall prediction is quantified using fuzzy logic. Organ segment weighted averages are computed to identify significant lesions. The model explains application of AI in medical imaging with an unprecedented level of detail. It can explain fine-grained image areas that have the greatest influence on diagnostic outcomes by mapping raw image pixels to fuzzy membership concepts. Lesions are found with effect sizes and statistical significance (p < 0.05).</div><div>Our model outperforms three existing methods in terms of interpretability and diagnostic accuracy by 10–15%, while maintaining computational efficiency. By disclosing crucial image evidence that supports AI decisions, this interpretable model improves transparency and clinician trust. Ethical implications of integrating AI in clinical settings are discussed, and future research directions are outlined. This study significantly advances the development of safe and interpretable AI for enhancing patient care through imaging analytics.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"35 ","pages":"Article 100535"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143129217","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}

引用次数: 0

EffSVMNet: An efficient hybrid neural network for improved skin disease classification EffSVMNet：改进皮肤病分类的高效混合神经网络

Smart Health Pub Date : 2024-12-01 Epub Date: 2024-10-05 DOI: 10.1016/j.smhl.2024.100520

Yash Sharma , Naveen Kumar Tiwari , Vipin Kumar Upaddhyay

{"title":"EffSVMNet: An efficient hybrid neural network for improved skin disease classification","authors":"Yash Sharma , Naveen Kumar Tiwari , Vipin Kumar Upaddhyay","doi":"10.1016/j.smhl.2024.100520","DOIUrl":"10.1016/j.smhl.2024.100520","url":null,"abstract":"<div><div>The Human Body’s primary defense layer is the skin which protects important organs from various external assaults. This organ protects our internal systems, safeguarding them from possible injury caused by viruses, fungus, and other factors. Unfortunately, the skin is not impenetrable, and infections or damage can occur, which leads to serious problems of health. Even a little skin lesion has the power to become a huge issue. As a result, in our study, our target is to produce an effective system for the quick and early identification of skin illnesses using well-known Convolutional Neural Networks (CNNs). The idea is to use this specialized neural network architecture to improve and speed up the detection and classification process to reduce time-lagging for treatment options. The proposed model <em>i.e.</em>, EffSVMNet is a hybrid model consisting of a CNN classifier similar to EfficientNet B3 architecture coupled with a support vector machine (SVM). The sample dataset containing four classes <em>i.e.</em>, acne, atopic dermatitis, bullous disease, and eczema is a subset of the DermNet dataset. The proposed model is not only lightweight but also achieves better validation accuracy when compared to similar methods in its category.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"34 ","pages":"Article 100520"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142416207","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}

引用次数: 0

A real-time eye movement-based computer interface for people with disabilities 基于眼动的实时残疾人计算机界面

Smart Health Pub Date : 2024-12-01 Epub Date: 2024-10-11 DOI: 10.1016/j.smhl.2024.100521

Ramazan Karatay, Burak Demir, Ali Arda Ergin, Erdem Erkan

{"title":"A real-time eye movement-based computer interface for people with disabilities","authors":"Ramazan Karatay, Burak Demir, Ali Arda Ergin, Erdem Erkan","doi":"10.1016/j.smhl.2024.100521","DOIUrl":"10.1016/j.smhl.2024.100521","url":null,"abstract":"<div><div>It is costly to develop systems that enable individuals exposed to Amyotrophic Lateral Sclerosis and similar diseases that directly affect the neuromotor ability to communicate with the outside world. In this study, a budget friendly, high-accuracy, software-based, gaze-controlled, real-time virtual keyboard approach that can enable these people to communicate effectively is proposed. The proposed application requires only a computer and a webcam and has a user-friendly interface that meets the basic daily needs of individuals with disabilities. Since the proposed system does not require an extra action such as blinking, it makes it possible to use computers in advanced stage patients who cannot blink their eyes. The application which uses a deep learning-based facial landmark detector, determines the letters the user focuses on the screen and converts thoughts into text. The part of the screen that the user focuses on is determined with a new selection approach inspired by the K-Nearest Neighbors algorithm. This approach, which does not require blinking, offers high speed and accuracy. In the tests, a typing speed of 23.33 characters per minute is achieved with an accuracy rate of 95.12%. It is anticipated that the study will increase computer accessibility for disabled individuals with limited mobility and contribute to the development of real-time eye tracking systems.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"34 ","pages":"Article 100521"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142444765","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}

引用次数: 0

Smart pain relief: Harnessing conservative Q learning for personalized and dynamic pain management 智能止痛：利用保守的 Q 学习技术实现个性化动态疼痛管理

Smart Health Pub Date : 2024-12-01 Epub Date: 2024-10-05 DOI: 10.1016/j.smhl.2024.100519

Yong Huang , Rui Cao , Thomas Hughes , Amir Rahmani

{"title":"Smart pain relief: Harnessing conservative Q learning for personalized and dynamic pain management","authors":"Yong Huang , Rui Cao , Thomas Hughes , Amir Rahmani","doi":"10.1016/j.smhl.2024.100519","DOIUrl":"10.1016/j.smhl.2024.100519","url":null,"abstract":"<div><div>Pain represents a multifaceted sensory and emotional experience often linked to tissue damage, bearing substantial healthcare costs and profound effects on patient well-being. Within intensive care units, effective pain management is paramount. However, determining suitable dosages of primary pain management drugs like morphine remains challenging due to their reliance on diverse patient-specific factors, including cardiovascular responses and pain intensity. To date, only a singular effort has explored personalized pain treatment recommendations through reinforcement learning. Regrettably, this pioneering study faced limitations stemming from incomplete patient state observations, a restricted action space, and the use of Deep Q-Networks, known for their sample inefficiency and lack of clinical interpretability. In our work, we introduced a Conservative Q-learning-based system for pain recommendation, enriching it with expanded state and action spaces. Additionally, we developed a comprehensive pipeline for both qualitative and quantitative evaluations, focusing on assessing the trained model’s performance. Our findings indicate a slight performance improvement over the clinician’s policy, offering a more clinically sensible and understandable approach compared to the current state-of-the-art methodologies.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"34 ","pages":"Article 100519"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142416208","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}

引用次数: 0

Improving health awareness with real-time monitoring through a three-dimensional visualized digital health avatar 通过三维可视化数字健康头像进行实时监测，提高健康意识

Smart Health Pub Date : 2024-12-01 Epub Date: 2024-11-06 DOI: 10.1016/j.smhl.2024.100522

Chaturapron Chokphukhiao , Pattrawan Pattaranit , Wonn Shweyi Thet Tun , Sakaowrat Masa , Rattikorn Leemananil , Nuttaphorn Natteerapong , Jutarop Phetcharaburanin , Sophon Boonlue , Khamron Sunat , Rina Patramanon

{"title":"Improving health awareness with real-time monitoring through a three-dimensional visualized digital health avatar","authors":"Chaturapron Chokphukhiao , Pattrawan Pattaranit , Wonn Shweyi Thet Tun , Sakaowrat Masa , Rattikorn Leemananil , Nuttaphorn Natteerapong , Jutarop Phetcharaburanin , Sophon Boonlue , Khamron Sunat , Rina Patramanon","doi":"10.1016/j.smhl.2024.100522","DOIUrl":"10.1016/j.smhl.2024.100522","url":null,"abstract":"<div><h3>Introduction</h3><div>The use of modern technologies has become crucial for enhancing people's awareness of health problems. By early health risk detection, there will be better treatment outcomes, the severity of illness will decrease, and costs for treatment will also reduce. Performing routine checkups could help patients feel less anxious about their health in the future. In this study, the innovation of “Digital Health Avatar” enables users to monitor physical changes, raise awareness of medical conditions, and encourage healthy behaviors by visualizing health status as a 3D figure.</div></div><div><h3>Methods</h3><div>Health data were collected using medical devices like blood test strips, body composition meters, and automatic blood pressure monitors. API technology and cloud computing systems were used to process these collected data and produced a three-dimensional avatar that indicated the user's health status. User satisfaction survey on using the health avatar system was assessed through a survey of 61 participants.</div></div><div><h3>Results</h3><div>Health avatar system successfully generate the data and display the measurement results on the health report page of the system. Users can easily check and record their physiological conditions through the avatar. Moreover, users showed satisfaction on the use of the health avatar system and its effectiveness in health check-ups in the survey.</div></div><div><h3>Conclusions</h3><div>The ‘Digital Health Avatar’ has the potential to significantly promote individual well-being and health awareness. Its development will be improved by user feedback and continued study, ensuring it becomes an efficient tool for promoting healthier lifestyles.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"34 ","pages":"Article 100522"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142704237","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}

引用次数: 0

TinyBioGait—Embedded intelligence and homologous time approximation warping for gait biometric authentication from IMU signals TinyBioGait--利用嵌入式智能和同源时间逼近经变技术对 IMU 信号进行步态生物识别认证

Smart Health Pub Date : 2024-12-01 Epub Date: 2024-08-28 DOI: 10.1016/j.smhl.2024.100515

Subhrangshu Adhikary , Subhadeep Biswas , Arindam Ghosh , Subrata Nandi

{"title":"TinyBioGait—Embedded intelligence and homologous time approximation warping for gait biometric authentication from IMU signals","authors":"Subhrangshu Adhikary , Subhadeep Biswas , Arindam Ghosh , Subrata Nandi","doi":"10.1016/j.smhl.2024.100515","DOIUrl":"10.1016/j.smhl.2024.100515","url":null,"abstract":"<div><p>The gait of a subject follows a specific pattern, but variations exist that are unique to a subject but contrasting to other subjects. This can be utilized for biometric authentication to prevent impersonation during gait studies. However, due to the dynamic nature of gait, like changes in gait speed while walking, gait biometric authentications are challenging. In the state-of-the-art, although attempts have been made to use deep learning and other signal processing methods for biometric authentication, which obtained reliable results, these are either highly resource-consuming, require several sensors or need an expensive framework, making it challenging to implement this in many scenarios. Therefore, a knowledge gap exists to build a reliable, inexpensive and resource-efficient gait biometric authentication system. The paper proposes a method for using only one embedded IMU sensor with a microcontroller for tracking the motion of a subject, resource-efficient on-device elimination of the gait speed differences by proposing a homologous time approximation warping algorithm and building a resource-efficient TinyML model for reliable biometric authentication. Based on an experiment consisting of 20 human subjects with consent, the microcontroller’s on-device accuracy score for decision-making by TinyML was found to be 0.9276. The resource efficiency of the model based on memory profiling has been further discussed. Also, the prediction performance of the microcontroller with the proposed optimization was found to be only 8% slower compared to a personal computer, given that several thousands of processes run parallel on a personal computer. The work needs to be further tested for a larger sample space, and data privacy needs to be addressed.</p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"34 ","pages":"Article 100515"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142094634","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}

引用次数: 0

PersonalPT: One-shot approach for skeletal-based repetitive action counting for physical therapy PersonalPT：用于物理治疗的基于骨骼的重复动作计数一次性方法

Smart Health Pub Date : 2024-12-01 Epub Date: 2024-10-01 DOI: 10.1016/j.smhl.2024.100516

Alexander Postlmayr, Bhanu Garg, Pamela Cosman, Sujit Dey

{"title":"PersonalPT: One-shot approach for skeletal-based repetitive action counting for physical therapy","authors":"Alexander Postlmayr, Bhanu Garg, Pamela Cosman, Sujit Dey","doi":"10.1016/j.smhl.2024.100516","DOIUrl":"10.1016/j.smhl.2024.100516","url":null,"abstract":"<div><div>There are thousands of physical therapy exercises which can be selected to tailor an individual’s rehabilitation program. In addition, exercises can be modified to accommodate a patient’s strength and range of motion as they recover and progress. The large size of the resulting set of exercises and their variations is problematic for current evaluation and feedback techniques, which are trained on a small number of exercises. Real-time exercise repetition counting, a core functionality for automated exercise feedback, is useful for promoting better health outcomes for physical therapy patients performing at-home exercises. We propose PersonalPT, a smartphone-based solution which can be used by physical therapists to customize individual patient treatment plans with a single training example. Our proposed one-shot exercise repetition segmentation model allows physical therapists to enable repetition counting on any exercise for individual patients based on their physical ability and rehabilitative needs. Our machine learning model outperforms other repetition counting algorithms (another semi-supervised and a supervised approach) on three exercise datasets. We demonstrate the feasibility of using computer vision and machine learning, on a smartphone, to perform repetition counting for exercises in real-time.</div></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"34 ","pages":"Article 100516"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142416357","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}

引用次数: 0