[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119637-en":3,"doc-seo-119637-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119637,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Machine Learning Assisted Postural Movement Recognition using Photoplethysmography(PPG) - Fall Detection Research","Growing elderly populations and rising care home admissions drive urgent demand for reliable fall detection and prevention technologies. This work applies machine learning to recognize postural movements using Photoplethysmography (PPG) data only. A custom device reads PPG signals, segments them into individual pulses, extracts pulse morphology and homeostatic characteristic features, and evaluates multiple ML classifiers. Experiments on 11 participants cover stationary, sitting-to-standing, and lying-to-standing transitions. Artificial Neural Networks achieve the strongest performance, reaching 85.2% testing accuracy and an F1 score of 78%.","Maccay, R. , & Weerasekera, R. (2024) . Machine Learning Assisted Postural Movement Recognition using Photoplethysmography(PPG) . [https://doi.org/10.48550/arXiv.2411.11862](https://doi.org/10.48550/arXiv.2411.11862)  \nEarly version, also known as pre-print  \nLink to published version (if available):  \n10.48550/arXiv.2411.11862  \nLink to publication record on the Bristol Research Portal  \nPDF-document  \nUniversity of Bristol – Bristol Research Portal  \nGeneral rights  \nThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Full terms of use are available: [http://www.bristol.ac.uk/red/research-policy/pure/user-guides/brp-terms/](http://www.bristol.ac.uk/red/research-policy/pure/user-guides/brp-terms/)  \narXiv :2411 . 11862v1 [ ee ss . SP] 2 Nov 2024  \nMACHINE LEARNING ASSISTED POSTURAL MOVEMENT RECOGNITION USING PHOTOPLETHYSMOGRAPHY(PPG) ∗  \nRobbie Maccay and Roshan Weerasekera  \nSchool of Electrical Electronic and Mechanical Engineering (EEME)  \nUniversity of Bristol  \nBristol, UK  \ncorresponding author: [roshan.weerasekera@bristol.ac.uk](roshan.weerasekera@bristol.ac.uk)  \nABSTRACT  \nWith the growing percentage of elderly people and care home admissions, there is an urgent need for the development of fall detection and fall prevention technologies. This work presents, for the first time, the use of machine learning techniques to recognize postural movements exclusively from Photoplethysmography (PPG) data. To achieve this goal, a device was developed for reading the PPG signal, segmenting the PPG signals into individual pulses, extracting pulse morphology and homeostatic characteristic features, and evaluating different ML algorithms. Investigations into different postural movements (stationary, sitting to standing, and lying to standing) were performed by 11 participants. The results of these investigations provided insight into the differences in homeostasis after the movements in the PPG signal. Various machine learning approaches were used for classification, and the Artificial Neural Network (ANN) was found to be the best classifier, with a testing accuracy of 85.2% and an F1 score of 78% from experimental results.  \nKeywords Artificial Intelligence · Fall detection · Photoplethysmography · Postural · Machine Learning · Wearables  \n1 Introduction  \nPeople are living longer, and the aging population of the UK is ever increasing. The UK currently has a population of 5.5 million people aged over 75, which is set to increase to 7.1 million by 2035 [1] . Of the 3.2 million of the 5.5 million people over the age of 80, half will have at least one fall a year [2] . These falls are caused due to many factors, including muscle weakness, poor balance or visual impairment [3] . With an elderly person falling every ten seconds in the UK, the prevalence of life altering injuries, such as head injuries and hip fractures, is high and can prove to be fatal [4] . These injuries can lead to individuals requiring hospitalisation and surgery. They result in a loss of confidence and anxiety of falls in the future, leading to them restricting their activities in their daily lives [4] and can result in requiring care home admission. This combination of physical and psychological impacts to the geriatric populations has led to falls being the ninth leading cause of disability-adjusted life years (DALYs) in England in 2013, putting a large strain on the National Health Service (NHS), costing £435 million annually in England alone for falls in the house [3] .  \nFall detection and fall prevention are two crucial strategies to reduce the prevalence of falls and allow the growing older population to maintain their independence. Fall detection is defined as the detection of a fall using sensors and cameras to summon help [4] . Fall prevention refers to systems to stop falls by observing the person’s movement [4] and actions to reduce the likelihood of falls. Fall preve","cbCaivpjAVpZlFPe","https://ap.wps.com/l/cbCaivpjAVpZlFPe","pdf",2946424,1,18,"English","en",105,"# Introduction\n## Fall detection and prevention needs\n## Postural monitoring in care settings\n# Methodology\n## PPG sensing, segmentation, and feature extraction\n## Machine learning classification approaches\n# Experiments\n## Participants and movement scenarios\n## Evaluation results and classifier performance\n# Discussion\n## Homeostasis differences in PPG signals","[{\"question\":\"What problem does the study address?\",\"answer\":\"It targets the need for fall detection and fall prevention technologies for an aging population, especially in care homes where monitoring is challenging.\"},{\"question\":\"How are postural movements recognized in the proposed approach?\",\"answer\":\"A device records PPG signals, segments them into pulses, extracts pulse morphology and homeostatic features, and feeds them into machine learning classifiers.\"},{\"question\":\"Which classifier performed best and what were the results?\",\"answer\":\"The Artificial Neural Network (ANN) performed best, with 85.2% testing accuracy and an F1 score of 78% based on experimental results.\"}]","Machine Learning Assisted Postural Movement Recognition using Photoplethysmography(PPG) - 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