J. Med. Internet Res. - A web-based non-intrusive ambient system to measure and classify activities of daily living.

Tópicos

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Resumo

CKGROUND: The number of older adults in the global population is increasing. This demographic shift leads to an increasing prevalence of age-associated disorders, such as Alzheimer's disease and other types of dementia. With the progression of the disease, the risk for institutional care increases, which contrasts with the desire of most patients to stay in their home environment. Despite doctors' and caregivers' awareness of the patient's cognitive status, they are often uncertain about its consequences on activities of daily living (ADL). To provide effective care, they need to know how patients cope with ADL, in particular, the estimation of risks associated with the cognitive decline. The occurrence, performance, and duration of different ADL are important indicators of functional ability. The patient's ability to cope with these activities is traditionally assessed with questionnaires, which has disadvantages (eg, lack of reliability and sensitivity). Several groups have proposed sensor-based systems to recognize and quantify these activities in the patient's home. Combined with Web technology, these systems can inform caregivers about their patients in real-time (e.g., via smartphone).OBJECTIVE: We hypothesize that a non-intrusive system, which does not use body-mounted sensors, video-based imaging, and microphone recordings would be better suited for use in dementia patients. Since it does not require patient's attention and compliance, such a system might be well accepted by patients. We present a passive, Web-based, non-intrusive, assistive technology system that recognizes and classifies ADL.METHODS: The components of this novel assistive technology system were wireless sensors distributed in every room of the participant's home and a central computer unit (CCU). The environmental data were acquired for 20 days (per participant) and then stored and processed on the CCU. In consultation with medical experts, eight ADL were classified.RESULTS: In this study, 10 healthy participants (6 women, 4 men; mean age 48.8 years; SD 20.0 years; age range 28-79 years) were included. For explorative purposes, one female Alzheimer patient (Montreal Cognitive Assessment score=23, Timed Up and Go=19.8 seconds, Trail Making Test A=84.3 seconds, Trail Making Test B=146 seconds) was measured in parallel with the healthy subjects. In total, 1317 ADL were performed by the participants, 1211 ADL were classified correctly, and 106 ADL were missed. This led to an overall sensitivity of 91.27% and a specificity of 92.52%. Each subject performed an average of 134.8 ADL (SD 75).CONCLUSIONS: The non-intrusive wireless sensor system can acquire environmental data essential for the classification of activities of daily living. By analyzing retrieved data, it is possible to distinguish and assign data patterns to subjects' specific activities and to identify eight different activities in daily living. The Web-based technology allows the system to improve care and provides valuable information about the patient in real-time.

Resumo Limpo

ckground number older adult global popul increas demograph shift lead increas preval ageassoci disord alzheim diseas type dementia progress diseas risk institut care increas contrast desir patient stay home environ despit doctor caregiv awar patient cognit status often uncertain consequ activ daili live adl provid effect care need know patient cope adl particular estim risk associ cognit declin occurr perform durat differ adl import indic function abil patient abil cope activ tradit assess questionnair disadvantag eg lack reliabl sensit sever group propos sensorbas system recogn quantifi activ patient home combin web technolog system can inform caregiv patient realtim eg via smartphoneobject hypothes nonintrus system use bodymount sensor videobas imag microphon record better suit use dementia patient sinc requir patient attent complianc system might well accept patient present passiv webbas nonintrus assist technolog system recogn classifi adlmethod compon novel assist technolog system wireless sensor distribut everi room particip home central comput unit ccu environment data acquir day per particip store process ccu consult medic expert eight adl classifiedresult studi healthi particip women men mean age year sd year age rang year includ explor purpos one femal alzheim patient montreal cognit assess score time go second trail make test second trail make test b second measur parallel healthi subject total adl perform particip adl classifi correct adl miss led overal sensit specif subject perform averag adl sd conclus nonintrus wireless sensor system can acquir environment data essenti classif activ daili live analyz retriev data possibl distinguish assign data pattern subject specif activ identifi eight differ activ daili live webbas technolog allow system improv care provid valuabl inform patient realtim

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