Application of machine learning to classify dementia wandering patterns...9th World Conference of the International Society for Gerontechnology (ISG), June 18-21, 2014, Taipei, Taiwan.
Purpose Wandering is a diagnostic sign of several psychiatric syndromes, including dementia and geriatric depression. Scientifically, wandering linked to dementia is characterized by pacing, lapping, and random locomotion1. We are developing an automated system for real-time recognition of wandering...
| Publicado en: | Gerontechnology Vol. 13; no. 2; pp. 294 - 295 |
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| Autores principales: | , , |
| Formato: | abstract proceedings research tables/charts Journal Article |
| Publicado: |
International Society for Gerontechnology
2014 Special Issue
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=180444928&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 180444928 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15691101 904R jtl: Gerontechnology issn: 15691101 maglogo: N pubinfo: dt: 2014 Special Issue vid: 13 iid: 2 pid: 54298 pub: International Society for Gerontechnology artinfo: ui: 180444928 180444928 180444928 10.4017/gt.2014.13.02.146.00 180444928 ppf: 294 ppct: 1 formats: tig: atl: Application of machine learning to classify dementia wandering patterns...9th World Conference of the International Society for Gerontechnology (ISG), June 18-21, 2014, Taipei, Taiwan. aug: au: VUONG, N. K. CHAN, S. LAU, C. T. affil: School of Computer Engineering, Nanyang Technological University, Singapore sug: subj: Machine Learning Dementia Patients Classification Wandering Behavior Rehabilitation Centers Day Care Systems Development Congresses and Conferences Taiwan Taiwan ab: Purpose Wandering is a diagnostic sign of several psychiatric syndromes, including dementia and geriatric depression. Scientifically, wandering linked to dementia is characterized by pacing, lapping, and random locomotion1. We are developing an automated system for real-time recognition of wandering patterns by multiple residents in dementia day care centres. This paper reports the progress of our feasibility study for the technology, and its early results. Method We conducted an experiment to detect patient travel patterns including direct, pacing, lapping, and random in a laboratory area (6mx4m). Using a commercially available system (Ubisense, Inc.), we collected movement trajectories comprising 155 travel patterns from 3 volunteers. We pre-processed the raw trajectories with a redundant distance filter and a motion average smoother to reject corrupt readings and smooth out short-term irregularities in the raw data. Figure 1 shows a lapping trajectory before and after pre-processing. We characterise each trajectory by a feature vector comprising the following information: displacement, path length, total travel time, average velocity, straightness index, directional mean, and circular variance2. The normalized feature vectors were used for supervised training machine learning algorithms to classify trajectories into direct, pacing, lapping and random locomotion. 6 classifiers were used: Bayes Net, Multilayer Perceptron (MLP), Bagging, Boosting, Decision Tree, and Random Forest3. Using 10-fold cross validation, the accuracy of each classifier was obtained by comparing the classification results with the ground truths. Results & Discussion Figure 2 reports the accuracy of each classifier for the entire data set. The best accuracy, 72%, is achieved by a Random Forest classifier. We conclude that machine learning is capable of detecting individuals' wandering patterns they move within a confined area. Currently, movement data from other volunteers are being collected to obtain normative estimates of diurnal travel pattern variability across different individuals. We are also exploring the recognition of other patterns - such as zigzag or figure eight types. Early results will be presented. The implications for the technology as a clinical application for wandering and early dementia detection are discussed. pubtype: Academic Journal doctype: abstract proceedings research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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