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

Stucki RA, Urwyler P, Rampa L, Müri R, Mosimann UP, Nef T - J. Med. Internet Res. (2014)

Bottom Line: To provide effective care, they need to know how patients cope with ADL, in particular, the estimation of risks associated with the cognitive decline.Since it does not require patient's attention and compliance, such a system might be well accepted by patients.The Web-based technology allows the system to improve care and provides valuable information about the patient in real-time.

View Article: PubMed Central - HTML - PubMed

Affiliation: Gerontechnology and Rehabilitation Group, University of Bern, Bern, Switzerland.

ABSTRACT

Background: 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.

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Block diagram of the ADL classifier algorithm based on a forward chaining inference engine.
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figure5: Block diagram of the ADL classifier algorithm based on a forward chaining inference engine.

Mentions: In the second step, the data were classified using an ADL classifier (Figure 5). The classification of the ADL is based on the assumption that each subject follows a daily routine, where specific patterns with nearly the same duration and course occur throughout each day [31]. Considering the fact that numerous data values are accumulating during the measurements, the classifier was implemented as a rule-based inference engine. The concept is ideal to handle numerous data, as it is widely used in the field of very-large-scale integration [32]. The theoretical concept was adapted to match the needs of the ADL classifier. It consists of (1) a database, (2) a rule-repository, and (3) a forward chaining inference engine. The database (1) holds all the data sorted upfront by the Bucketsort and Radixsort algorithms, but also all the classified ADL so far (historical data). The rule-repository (2) provides the forward chaining inference engine with a set of parameterized behavioral knowledge (the parameterization was done in cooperation with our medical experts.) A parser translates the parameterized behavioral knowledge into a look-up table disposable in the random access memory. The (3) forward chaining inference engine charges all available facts according to the given rules. The rules were defined manually and were the same for all subjects. The daily routine itself (ie, the time of the activity) was not considered within the rules; rather, the rules were applied to the daily routine resulting from the specific behavior pattern throughout the day. Nevertheless, the forward chaining inference engine needs some conflict resolution strategy to decide which information is the most important to process first and in which order the rest of the information has to be taken into account. Going the other way, the forward chaining inference engine checks which condition information must be fulfilled to state the given information as one specific ADL.


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

Stucki RA, Urwyler P, Rampa L, Müri R, Mosimann UP, Nef T - J. Med. Internet Res. (2014)

Block diagram of the ADL classifier algorithm based on a forward chaining inference engine.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4129128&req=5

figure5: Block diagram of the ADL classifier algorithm based on a forward chaining inference engine.
Mentions: In the second step, the data were classified using an ADL classifier (Figure 5). The classification of the ADL is based on the assumption that each subject follows a daily routine, where specific patterns with nearly the same duration and course occur throughout each day [31]. Considering the fact that numerous data values are accumulating during the measurements, the classifier was implemented as a rule-based inference engine. The concept is ideal to handle numerous data, as it is widely used in the field of very-large-scale integration [32]. The theoretical concept was adapted to match the needs of the ADL classifier. It consists of (1) a database, (2) a rule-repository, and (3) a forward chaining inference engine. The database (1) holds all the data sorted upfront by the Bucketsort and Radixsort algorithms, but also all the classified ADL so far (historical data). The rule-repository (2) provides the forward chaining inference engine with a set of parameterized behavioral knowledge (the parameterization was done in cooperation with our medical experts.) A parser translates the parameterized behavioral knowledge into a look-up table disposable in the random access memory. The (3) forward chaining inference engine charges all available facts according to the given rules. The rules were defined manually and were the same for all subjects. The daily routine itself (ie, the time of the activity) was not considered within the rules; rather, the rules were applied to the daily routine resulting from the specific behavior pattern throughout the day. Nevertheless, the forward chaining inference engine needs some conflict resolution strategy to decide which information is the most important to process first and in which order the rest of the information has to be taken into account. Going the other way, the forward chaining inference engine checks which condition information must be fulfilled to state the given information as one specific ADL.

Bottom Line: To provide effective care, they need to know how patients cope with ADL, in particular, the estimation of risks associated with the cognitive decline.Since it does not require patient's attention and compliance, such a system might be well accepted by patients.The Web-based technology allows the system to improve care and provides valuable information about the patient in real-time.

View Article: PubMed Central - HTML - PubMed

Affiliation: Gerontechnology and Rehabilitation Group, University of Bern, Bern, Switzerland.

ABSTRACT

Background: 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.

Show MeSH
Related in: MedlinePlus