Healthcare Technology Letters最新文献

{"title":"Next-Generation Human Activity Recognition Using Locality Constrained Linear Coding Combined With Machine Learning (NG-HAR-LCML)","authors":"Maryam Shabbir,&nbsp;Fahad Ahmad,&nbsp;Saad Awadh Alanazi,&nbsp;Muhammad Hassan Khan,&nbsp;Jianqiang Li,&nbsp;Tariq Mahmood,&nbsp;Shahid Naseem,&nbsp;Muhammad Anwar","doi":"10.1049/htl2.70013","DOIUrl":"https://doi.org/10.1049/htl2.70013","url":null,"abstract":"<p>Accurate Human Activity Recognition (HAR) is a critical challenge with wide-ranging applications in healthcare, assistive technologies, and human-computer interaction. Traditional feature extraction methods often struggle to capture the complex spatial and temporal dynamics of human movements, leading to suboptimal classification performance. To address this limitation, this study introduces a novel encoding approach using Locality-Constrained Linear Coding (LLC) to enhance the discriminative power of hand-crafted features extracted from low-cost wearable sensors—an accelerometer and a gyroscope. The proposed LLC-based encoding scheme enables robust feature representation, improving the accuracy of HAR models. The encoded features are classified using a diverse set of Machine Learning (ML) and Deep Learning (DL) algorithms, including Support Vector Machine (SVM), Logistic Regression (LR), Naive Bayes (NB), K-Nearest Neighbours (KNN), AdaBoost, Gradient Boosting Machine (GBM), and Deep Belief Network (DBN). Extensive quantitative evaluations demonstrate that LLC significantly outperforms conventional feature encoding techniques, leading to improved classification accuracy. Among the tested models, DBN achieves a state-of-the-art accuracy of 99%, highlighting its superiority for HAR tasks. The contributions of this research are threefold: (1) it establishes the necessity of an advanced encoding scheme (LLC) for feature enhancement in HAR, (2) it provides a rigorous comparative analysis of multiple ML and DL classifiers, and (3) it introduces a scalable and cost-effective HAR framework suitable for real-world applications. Performance is comprehensively assessed using robust evaluation metrics such as Area Under the Curve (AUC), Classification Accuracy (CA), F1 Score, Precision, Recall, and Matthews Correlation Coefficient (MCC). The findings of this study offer new insights into feature encoding for HAR, setting a foundation for future advancements in sensor-based activity recognition.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/htl2.70013","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144085275","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":"An Analysis of Monitoring Solutions for CAR T Cell Production","authors":"Arber Shoshi,&nbsp;Yuchen Xia,&nbsp;Andrea Fieschi,&nbsp;Yannick Baumgarten,&nbsp;Andrea Gaißler,&nbsp;Thomas Ackermann,&nbsp;Peter Reimann,&nbsp;Bernhard Mitschang,&nbsp;Michael Weyrich,&nbsp;Thomas Bauernhansl,&nbsp;Robert Miehe","doi":"10.1049/htl2.70012","DOIUrl":"https://doi.org/10.1049/htl2.70012","url":null,"abstract":"<p>The chimeric antigen receptor T cell (CAR T) therapy has shown remarkable results in treating certain cancers. It involves genetically modifying a patient's T cells to recognize and attack cancer cells. Despite its potential, CAR T cell therapy is complex and costly and requires the integration of multiple technologies and specialized equipment. Further research is needed to achieve the maximum potential of CAR T cell therapies and to develop effective and efficient methods for their production. This paper presents an overview of current measurement methods used in the key steps of the production of CAR T cells. The study aims to assess the state of the art in monitoring solutions and identify their potential for online monitoring. The results of this paper contribute to the understanding of measurement methods in CAR T cell manufacturing and identify areas where on-line monitoring can be improved. Thus, this research facilitates progress toward the development of effective monitoring of CAR T cell therapies.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/htl2.70012","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143944573","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":"The Impact of Excessive Muscle Co-Contraction on Sit-To-Stand Performance in High-Heeled Footwear","authors":"Ganesh R. Naik,&nbsp;Amit N. Pujari","doi":"10.1049/htl2.70011","DOIUrl":"https://doi.org/10.1049/htl2.70011","url":null,"abstract":"<p>This study aimed to analyse the effects of co-contraction on quadriceps and hamstring muscles during sit-to-stand (STS) tasks for females wearing shoes with different heel heights. The study aimed to identify compensatory strategies during the STS tasks in response to excessive muscle co-contraction during high-heeled gait. Sixteen healthy young women (age: 24.4 ± 1.7 years, body mass index: 18.4 ± 1 kg/m², weight: 50.2 ± 5.2 kg, height: 1.63 ± 4.4 m) participated in this study. Electromyography signals were recorded from three quadriceps (vastus medialis, vastus lateralis, and rectus femoris) and one hamstring (semitendinosus) muscles. The participants wore shoes with different heights, including 4, 6, 8, and 10 cm. For each heel height, the co-contraction index is computed to measure postural balance using the quadriceps to hamstring muscle pairs. The results that were obtained and quantified with statistical measures show that for elevated shoes, if co-contraction increases, both quadriceps and hamstring muscles tend to compensate. This suggests that the capacity of the quadriceps and hamstring muscles to compensate is essential to retain normal walking and STS tasks in co-contracted persons. However, the compensation mechanisms may induce imbalance, muscle stiffness, and fatigue for women who regularly use high-heeled shoes during sit-to-stand tasks.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/htl2.70011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143925827","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 Self-Monitoring Mobile App to Mitigate Risk Factors for Suicide and Self-Harm in Junior (Resident) Doctors: A Review, Thematic Analysis and Concept Proposal","authors":"Kirsten Leslie,&nbsp;Chloe Sawyer,&nbsp;Katy Oak,&nbsp;Gareth Lewis,&nbsp;Bryan Clark,&nbsp;Anna Mankee-Williams,&nbsp;Ellen Wilkinson,&nbsp;Hiu Lam,&nbsp;Richard Laugharne,&nbsp;Rohit Shankar","doi":"10.1049/htl2.70009","DOIUrl":"https://doi.org/10.1049/htl2.70009","url":null,"abstract":"<p>Doctors, particularly those in training in the UK, are exposed to high levels of stress in their work, which can lead to burnout and mental health problems. According to the health and safety executive (HSE) Management UK standards, employers should recognise and minimise work-related stress for staff. Our review looks to examine if known risk factors for suicide and self-harm in doctors align with the themes of the HSE management standards on stress control i.e., demand, control, support, relationships, role, and change and if so, could this be used to build a self-awareness digital application. Four research databases were searched using combinations of text words and thesaurus terms and predefined inclusion/exclusion criteria for relevant article retrieval. A thematic analysis was undertaken, aligning articles to their respective HSE standards. Twenty-six articles met the full inclusion criteria. 96.2% (25/26 papers) mentioned or aligned at least one of the HSE management standards, with 44% discussing three or more. Work-related risk factors for self-harm and suicide in doctors link well to the HSE management standards. We conceptualise a self-monitoring digital well-being tool for doctors to monitor stress.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/htl2.70009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143914511","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":"Comparative Process Mining for Identifying the Critical Activities in Sepsis Trajectories","authors":"Mohsen Mohammadi","doi":"10.1049/htl2.70010","DOIUrl":"https://doi.org/10.1049/htl2.70010","url":null,"abstract":"<p>Sepsis, a life-threatening condition with high mortality and readmission rates, demands precise and timely management to improve patient outcomes. Despite advancements, identifying the critical activities within sepsis treatment pathways remains a challenge, limiting the effectiveness of interventions. This study addresses this issue by utilising comparative process mining techniques to analyse sepsis trajectories, focusing on key performance metrics—sojourn time, arrival rate and finish rate—across distinct patient clusters. The analysis is based on real-life event logs from a hospital's sepsis cases, using K-means clustering to segment patients by age, severity and key clinical indicators. The study reveals critical activities such as ‘Return ER’, ‘Admission IC’, and ‘Release C’, which consistently exhibit high sojourn times and influence patient outcomes significantly. These activities emerge as bottlenecks in the patient care process, particularly in cases of severe sepsis, where delays can lead to increased complications and mortality. By identifying these critical points, the study provides actionable insights for healthcare providers to optimize resource allocation, reduce delays and enhance the overall efficiency of sepsis management. The findings underscore the importance of targeted interventions in these key areas, offering a path toward improved clinical outcomes and reduced sepsis-related mortality and readmission rates. This research contributes to the growing field of process mining in healthcare, highlighting its potential to transform complex clinical pathways into more efficient and effective treatment processes.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/htl2.70010","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143904984","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":"Hybrid deep learning method to identify key genes in autism spectrum disorder","authors":"Naveen Kumar Singh,&nbsp;Asmita Patel,&nbsp;Nidhi Verma,&nbsp;R. K. Brojen Singh,&nbsp;Saurabh Kumar Sharma","doi":"10.1049/htl2.12104","DOIUrl":"https://doi.org/10.1049/htl2.12104","url":null,"abstract":"<p>Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with a strong genetic component. This research aims to identify key genes associated with autism spectrum disorder using a hybrid deep learning approach. To achieve this, a protein–protein interaction network is constructedand analyzed through a graph convolutional network, which extracts features based on gene interactions. Logistic regression is then employed to predict potential key regulatorgenes using probability scores derived from these features. To evaluate the infection ability of these potential key regulator genes, a susceptible–infected (SI) model, is performed, which reveals the higher infection ability for the genes identified by the proposed method, highlighting its effectiveness in pinpointing key genetic factors associated with ASD. The performance of the proposed method is compared with centrality methods, showing significantly improved results. Identified key genes are further compared with the SFARI gene database and the Evaluation of Autism Gene Link Evidence (EAGLE) framework, revealing commongenes that are strongly associated with ASD. This reinforces the validity of the method in identifying key regulator genes. The proposed method aligns with advancements in therapeutic systems, diagnostics, and neural engineering, providing a robust framework for ASD research and other neurodevelopmental disorders.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/htl2.12104","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143871665","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":"The Bionics Bus for Neurology and Neuropsychiatry: Concept Development and Validation","authors":"Christopher James,&nbsp;Sarang Shankar,&nbsp;Samuel J. Tromans,&nbsp;Richard Laugharne,&nbsp;Paraskevi Triantafyllopoulou,&nbsp;Maria Richards,&nbsp;Rohit Shankar","doi":"10.1049/htl2.70008","DOIUrl":"https://doi.org/10.1049/htl2.70008","url":null,"abstract":"<p>Healthcare delivery in the United Kingdom is increasingly becoming a challenging issue where demand is regularly outstripping availability. This is particularly a challenge in neurology and neuropsychiatry, where delays in diagnosis and treatment are leading to worse health and social outcomes. The Darzi report, which focused on three key tenants, has been hailed as the future blueprint for National Health Service (NHS) sustainability and high-quality care delivery. These three tenants are moving from analogue to digital approaches, focusing on prevention and wellbeing, and supporting diagnosis and treatment in communities instead of hospitals. Technological interventions are relevant at all stages of these care pathways. There is an opportunity to identify an easy to use community-based mobile resource to help screen, triage and refer suspect neurology and neuropsychiatric presentations to the right support. The potential benefits to patients, clinicians, organisations and communities could be significant. To enable this vision, the concept of Bionic Bus (https://bionicsbus.org/) was developed. This study looked to understand the acceptability, utility and scope of the Bionics Bus concept among the public using mixed-methods research techniques. Results suggest high acceptability, utility and wide scope. This study gives a template for similar evidence-based innovation to be applied for other health conditions.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/htl2.70008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143688922","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":"Differential analysis of brain functional network parameters in MHE patients","authors":"Li Song,&nbsp;Yiting Zhang,&nbsp;Xiaoyan Wang,&nbsp;Xucai Ji","doi":"10.1049/htl2.70004","DOIUrl":"https://doi.org/10.1049/htl2.70004","url":null,"abstract":"<p>Resting-state functional magnetic resonance imaging, using blood-oxygen-level-dependence signal data and graph theory, was employed to explore brain functional network parameter changes in 32 MHE patients and 21 healthy controls. The Gretna software package and spm8 are used to preprocess and process the data in matlab2012b to calculate the global efficiency (Eg), local efficiency (El), nodal degree (nodal De), nodal clustering coefficient (nodal Cp), nodal shortest path length (nodal Lp), and nodal betweenness (nodal Be) as brain functional network characteristic parameters. The BrainNet View soft is used to draw network maps and present surface-based data. Within the sparsity range of the selected network, A double-sample t-test revealed significant differences about the characteristic parameters in the following brain regions: the Nodal Cp in AAL62, AAL26, AAL43, and AAL47; the De in AAL66, AAL68, AAL47, and AAL74; the nodal Lp in AAL28, the El in AAL62, AAL31, and AAL47; the Eg in AAL28, AAL32, and AAL51, and the nodal Be in AAL28, AAL32, AAL76, and AAL82. These changes in brain network nodes may signal early brain damage in MHE, helping to characterize MHE and predict mental decline in cirrhosis patients.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/htl2.70004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143554821","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":"The Feasibility of Ambulatory Heart Rate Variability Monitoring in Non-Suicidal Self-Injury","authors":"Alje van Hoorn,&nbsp;Anna Mankee-Williams,&nbsp;Gareth Lewis,&nbsp;Rafaella Mellili,&nbsp;Jessica Eccles,&nbsp;Cristina Ottaviani,&nbsp;Richard Laugharne,&nbsp;Rohit Shankar","doi":"10.1049/htl2.70007","DOIUrl":"https://doi.org/10.1049/htl2.70007","url":null,"abstract":"<p>The polyvagal theory proposes that the autonomic nervous system influences affective systems and top-down emotional regulation. Vagal tone, as indexed by heart rate variability (HRV), is a measure of emotion regulation capacity. It is possible that non-suicidal self-injury (NSSI) occurs at times of low vagal tone and that NSSI may increase it. Little is known about the feasibility of collecting ambulatory HRV data in the context of NSSI. This prospective observational study examined the feasibility of ambulatory HRV monitoring during NSSI. Ten participants wore a chest-based heart rate monitor and used a diary app for 1 week. Baseline characteristics were collected. Heart rate monitoring duration, diary app entries, distress scores, and NSSI occurrences were recorded. Participant experience was assessed in a post-study questionnaire. At baseline, six had a history of NSSI, in two of whom it was current. Ten participants wore the monitor for an average of 137 h. Nine participants successfully used the diary app, making an average of 14 entries over a week. Although no NSSI occurred during the study, the overall experience of participation was positive. It is feasible to monitor HRV and collect app-based distress scores for a week, including in those with NSSI history.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/htl2.70007","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143513857","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":"Signal-quality-aware multisensor fusion for atrial fibrillation detection","authors":"Shane Malone,&nbsp;Barry Cardiff,&nbsp;Deepu John,&nbsp;Arlene John","doi":"10.1049/htl2.12121","DOIUrl":"https://doi.org/10.1049/htl2.12121","url":null,"abstract":"<p>This letter introduces a novel method to enhance atrial fibrillation detection accuracy in healthcare monitoring. Wearable devices often face inconsistent signal quality due to noise. To address this, a multimodal data fusion technique that improves signal reliability during continuous monitoring is proposed. The method improves the precision of detecting R–R intervals by integrating wavelet coefficients from electrocardiogram, photoplethysmogram, and arterial blood pressure signals, weighted according to the quality of each signal. Furthermore, a bi-directional long short-term memory network is developed to accurately detect AF based on the derived heartrate or R–R intervals. Unlike prior studies, this work uniquely evaluates the system’s performance under noisy conditions, demonstrating significant accuracy improvements over single-channel methods. The system's generalizability is confirmed by evaluating the classifier's performance as the number of sensor inputs increases. At a signal-to-noise ratio of −10 dB, the accuracy improves by 4.51% with two sensor inputs and by 10.92% with three inputs, compared to using a single input.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/htl2.12121","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489697","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}