Evaluation of Three State-of-the-Art Classifiers for Recognition of Activities of Daily Living from Smart Home Ambient Data

Smart homes for the aging population have recently started attracting the attention of the research community. The "health state" of smart homes is comprised of many different levels; starting with the physical health of citizens, it also includes longer-term health norms and outcomes, as well as the arena of positive behavior changes. One of the problems of interest is to monitor the activities of daily living (ADL) of the elderly, aiming at their protection and well-being. For this purpose, we installed passive infrared (PIR) sensors to detect motion in a specific area inside a smart apartment and used them to collect a set of ADL. In a novel approach, we describe a technology that allows the ground truth collected in one smart home to train activity recognition systems for other smart homes. We asked the users to label all instances of all ADL only once and subsequently applied data mining techniques to cluster in-home sensor firings. Each cluster would therefore represent the instances of the same activity. Once the clusters were associated to their corresponding activities, our system was able to recognize future activities. To improve the activity recognition accuracy, our system preprocessed raw sensor data by identifying overlapping activities. To evaluate the recognition performance from a 200-day dataset, we implemented three different active learning classification algorithms and compared their performance: naive Bayesian (NB), support vector machine (SVM) and random forest (RF). Based on our results, the RF classifier recognized activities with an average specificity of 96.53%, a sensitivity of 68.49%, a precision of 74.41% and an F-measure of 71.33%, outperforming both the NB and SVM classifiers. Further clustering markedly improved the results of the RF classifier. An activity recognition system based on PIR sensors in conjunction with a clustering classification approach was able to detect ADL from datasets collected from different homes. Thus, our PIR-based smart home technology could improve care and provide valuable information to better understand the functioning of our societies, as well as to inform both individual and collective action in a smart city scenario.

Evaluation of pet contact as a risk factor for carriage of multidrug-resistant staphylococci in nursing home residents

BACKGROUND Pets, often used as companionship and for psychological support in the therapy of nursing home residents, have been implicated as reservoirs for antibiotic-resistant bacteria. We investigated the importance of pets as reservoirs of multidrug-resistant (MDR) staphylococci in nursing homes. METHODS We assessed the carriage of MDR staphylococci in pets and in 2 groups of residents, those living in nursing homes with pets and those living without pet contacts. We collected demographic, health status, and human-pet contact data by means of questionnaires. We assessed potential bacteria transmission pathways by investigating physical resident-to-pet contact. RESULTS The observed prevalence of MDR staphylococci carriage was 84/229 (37%) in residents living with pets and 99/216 (46%) in those not living with pets (adjusted odds ratio [aOR], 0.6; 95% confidence interval [CI], 0.4-0.9). Active pet contact was associated with lower carriage of MDR staphylococci (aOR, 0.5; 95% CI, 0.4-0.8). Antibiotic treatment during the previous 3 months was associated with significantly increased risk for MDR carriage in residents (aOR, 3.1; 95% CI, 1.8-5.7). CONCLUSIONS We found no evidence that the previously reported benefits of pet contact are compromised by the increased risk of carriage of MDR staphylococci in residents associated with interaction with these animals in nursing homes. Thus, contact with pets, always under good hygiene standards, should be encouraged in these settings.

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

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.

Home-based training to improve manual dexterity in patients with multiple sclerosis: A randomized controlled trial.

BACKGROUND Impaired manual dexterity is frequent and disabling in patients with multiple sclerosis (MS), affecting activities of daily living (ADL) and quality of life. OBJECTIVE We aimed to evaluate the effectiveness of a standardized, home-based training program to improve manual dexterity and dexterity-related ADL in MS patients. METHODS This was a randomized, rater-blinded controlled trial. Thirty-nine MS patients acknowledging impaired manual dexterity and having a pathological Coin Rotation Task (CRT), Nine Hole Peg Test (9HPT) or both were randomized 1:1 into two standardized training programs, the dexterity training program and the theraband training program. Patients trained five days per week in both programs over a period of 4 weeks. Primary outcome measures performed at baseline and after 4 weeks were the CRT, 9HPT and a dexterous-related ADL questionnaire. Secondary outcome measures were the Chedoke Arm and Hand Activity Inventory (CAHAI-8) and the JAMAR test. RESULTS The dexterity training program resulted in significant improvements in almost all outcome measures at study end compared with baseline. The theraband training program resulted in mostly non-significant improvements. CONCLUSION The home-based dexterity training program significantly improved manual dexterity and dexterity-related ADL in moderately disabled MS patients. Trial Registration NCT01507636.

Recognition of activities of daily living in healthy subjects using two ad-hoc classifiers

Activities of daily living (ADL) are important for quality of life. They are indicators of cognitive health status and their assessment is a measure of independence in everyday living. ADL are difficult to reliably assess using questionnaires due to self-reporting biases. Various sensor-based (wearable, in-home, intrusive) systems have been proposed to successfully recognize and quantify ADL without relying on self-reporting. New classifiers required to classify sensor data are on the rise. We propose two ad-hoc classifiers that are based only on non-intrusive sensor data. METHODS: A wireless sensor system with ten sensor boxes was installed in the home of ten healthy subjects to collect ambient data over a duration of 20 consecutive days. A handheld protocol device and a paper logbook were also provided to the subjects. Eight ADL were selected for recognition. We developed two ad-hoc ADL classifiers, namely the rule based forward chaining inference engine (RBI) classifier and the circadian activity rhythm (CAR) classifier. The RBI classifier finds facts in data and matches them against the rules. The CAR classifier works within a framework to automatically rate routine activities to detect regular repeating patterns of behavior. For comparison, two state-of-the-art [Naïves Bayes (NB), Random Forest (RF)] classifiers have also been used. All classifiers were validated with the collected data sets for classification and recognition of the eight specific ADL. RESULTS: Out of a total of 1,373 ADL, the RBI classifier correctly determined 1,264, while missing 109 and the CAR determined 1,305 while missing 68 ADL. The RBI and CAR classifier recognized activities with an average sensitivity of 91.27 and 94.36%, respectively, outperforming both RF and NB. CONCLUSIONS: The performance of the classifiers varied significantly and shows that the classifier plays an important role in ADL recognition. Both RBI and CAR classifier performed better than existing state-of-the-art (NB, RF) on all ADL. Of the two ad-hoc classifiers, the CAR classifier was more accurate and is likely to be better suited than the RBI for distinguishing and recognizing complex ADL.