Limb apraxia in multiple sclerosis: prevalence and impact on manual dexterity and activities of daily living

To evaluate the prevalence and impact of limb apraxia on manual dexterity and activities of daily living (ADLs) in patients with multiple sclerosis (MS).

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.

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.

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.

Outcomes after stroke: Basic and instrumental activities of daily living, community reintegration and generic health status

This study describes the discharge destination, basic and instrumental activities of daily living (ADL), community reintegration and generic health status of people after stroke, and explored whether sociodemographic and clinical characteristics were associated with these outcomes. Participants were 51 people, with an initial stroke, admitted to an acute hospital and discharged to the community. Admission and discharge data were obtained by chart review. Follow-up status was determined by telephone interview using the Modified Barthel Index, the Assessment of Living Skills and Resources, the Reintegration to Normal Living Index, and the Short-Form Health Survey (SF-36). At follow up, 57% of participants were independent in basic ADL, 84% had a low risk of experiencing instrumental ADL difficulties, most had few concerns with community reintegration, and SF-36 physical functioning and vitality scores were lower than normative values. At follow up, poorer discharge basic ADL status was associated with poorer instrumental ADL and community reintegration status, and older participants had poorer instrumental ADL, community reintegration and physical functioning. Occupational therapists need to consider these outcomes when planning inpatient and post-discharge intervention for people after stroke.

Recognition of Activities of Daily Living from Topic Model

Research in ubiquitous and pervasive technologies have made it possible to recognise activities of daily living through non-intrusive sensors. The data captured from these sensors are required to be classified using various machine learning or knowledge driven techniques to infer and recognise activities. The process of discovering the activities and activity-object patterns from the sensors tagged to objects as they are used is critical to recognising the activities. In this paper, we propose a topic model process of discovering activities and activity-object patterns from the interactions of low level state-change sensors. We also develop a recognition and segmentation algorithm to recognise activities and recognise activity boundaries. Experimental results we present validates our framework and shows it is comparable to existing approaches.

Increasing body weight and risk of limitations in activities of daily living: a systematic review and meta-analysis

This study examined the relationship between normal weight, overweight and obesity class I and II+, and the risk of disability, which is defined as impairment in activities of daily living (ADL). Systematic searching of the literature identified eight cross-sectional studies and four longitudinal studies that were comparable for meta-analysis. An additional four cross-sectional studies and one longitudinal study were included for qualitative review. Results from the meta-analysis of cross-sectional studies revealed a graded increase in the risk of ADL limitations from overweight (1.04, 95% confidence interval [CI] 1.00-1.08), class I obesity (1.16, 95% CI 1.11-1.21) and class II+ obesity (1.76, 95% CI 1.28-2.41), relative to normal weight. Meta-analyses of longitudinal studies revealed a similar graded relationship; however, the magnitude of this relationship was slightly greater for all body mass index categories. Qualitative analysis of studies that met the inclusion criteria but were not compatible for meta-analysis supported the pooled results. No studies identified met all of the pre-defined quality criteria, and subgroup analysis was inhibited due to insufficient comparable studies. We conclude that increasing body weight increases the risk of disability in a graded manner, but also emphasize the need for additional studies using contemporary longitudinal cohorts with large numbers of obese class III individuals, a range of ages and with measured height and weight, and incident ADL questions.

Abuse and neglect of elderly and independence in instrumental and basic activities of daily living.

Introduction: One of the known risk factors for abuse and neglect of the elderly is the decrease in functional capacity, contributing to self care dependency of instrumental activities of daily living and basic activities of daily living (OMS, 2015). Methods: Cross-sectional study with non probabilistic sample of 333 elderly, performed in a hospital, homes and day centers for the elderly. The data collection protocol included socio-demographic data, Questions to elicit Elder Abuse (Carney, Kahan & Paris, 2003 adap. By Ferreira Alves & Sousa, 2005), scale of instrumental activities of daily living Lawton and Brody and Katz index to assess the level of independence in activities of daily living. Objectives: To evaluate the association between abuse and neglect in the elderly, instrumental activities of daily living and level of independence in activities of daily living. Results: Emotional abuse is significantly correlated with the level of independence in activities of daily living (p = 0.000), older people with less independence tend to have higher levels of emotional abuse. The total abuse is significantly correlated with the levels of independence in activities of daily living (p = 0.002), less independent elderly tend to suffer greater abuse and neglect. There were no statistically significant associations between abuse and neglect and instrumental activities of daily living. Conclusions: The less independent elderly are more vulnerable to situations of abuse and neglect, being more exposed to emotional abuse. These results point to the need for health professionals/ nurses develop prevention interventions, including strategies to support carers and early screening in less independent elderly.

Abuse and neglect of elderly and independence in instrumental and basic activities of daily living

Introduction: One of the known risk factors for abuse and neglect of the elderly is the decrease in functional capacity, contributing to self care dependency of instrumental activities of daily living and basic activities of daily living (OMS, 2015). Methods: Cross-sectional study with non probabilistic sample of 333 elderly, performed in a hospital, homes and day centers for the elderly. The data collection protocol included socio-demographic data, Questions to elicit Elder Abuse (Carney, Kahan & Paris, 2003 adap. By Ferreira Alves & Sousa, 2005), scale of instrumental activities of daily living Lawton and Brody and Katz index to assess the level of independence in activities of daily living. Objectives: To evaluate the association between abuse and neglect in the elderly, instrumental activities of daily living and level of independence in activities of daily living. Results: Emotional abuse is significantly correlated with the level of independence in activities of daily living (p = 0.000), older people with less independence tend to have higher levels of emotional abuse. The total abuse is significantly correlated with the levels of independence in activities of daily living (p = 0.002), less independent elderly tend to suffer greater abuse and neglect. There were no statistically significant associations between abuse and neglect and instrumental activities of daily living. Conclusions: The less independent elderly are more vulnerable to situations of abuse and neglect, being more exposed to emotional abuse. These results point to the need for health professionals/ nurses develop prevention interventions, including strategies to support carers and early screening in less independent elderly.

Passive wireless sensor systems can recognize activites of daily living

The ability to determine what activity of daily living a person performs is of interest in many application domains. It is possible to determine the physical and cognitive capabilities of the elderly by inferring what activities they perform in their houses. Our primary aim was to establish a proof of concept that a wireless sensor system can monitor and record physical activity and these data can be modeled to predict activities of daily living. The secondary aim was to determine the optimal placement of the sensor boxes for detecting activities in a room. A wireless sensor system was set up in a laboratory kitchen. The ten healthy participants were requested to make tea following a defined sequence of tasks. Data were collected from the eight wireless sensor boxes placed in specific places in the test kitchen and analyzed to detect the sequences of tasks performed by the participants. These sequence of tasks were trained and tested using the Markov Model. Data analysis focused on the reliability of the system and the integrity of the collected data. The sequence of tasks were successfully recognized for all subjects and the averaged data pattern of tasks sequences between the subjects had a high correlation. Analysis of the data collected indicates that sensors placed in different locations are capable of recognizing activities, with the movement detection sensor contributing the most to detection of tasks. The central top of the room with no obstruction of view was considered to be the best location to record data for activity detection. Wireless sensor systems show much promise as easily deployable to monitor and recognize activities of daily living.

Electronic aids to daily living: be able to do what you want

Purpose. This study explores the experiences of Irish people with high cervical spinal cord injuries living with electronic aids to daily living (EADL) and the meaning attributed to such systems in the context of participation in everyday life. Method. Qualitative methodology using a phenomenological approach was used to explore the phenomenon of living with EADL. Data were collected using four focus groups of users and nonusers of EADL (n = 15). All participants had high cervical spinal cord injuries (C3-5). Groups were video recorded, transcribed verbatim and analysed using descriptive phenomenological analysis. Findings. Findings revealed key elements of the meaning of living with EADL. Two key themes, time alone and changed relationships are described. These contribute to the super ordinate theme of autonomy. Findings suggest that participants perceived improvements in both anticipated and actual lived experiences with EADL. Themes are interrelated and together represent a summary of the experience of living with environmental controls. The themes described are similar to those found in other spinal injury studies relating to quality of life. Conclusions. Findings highlight differences in life experiences for those with and without EADL and provides motivation to address this difference. Such insights are valuable for both users and providers of EADL.

Experiences and desires of people with tetraplegia living with and without electronic aids to daily living: an Irish focus group study

This qualitative descriptive study explores the lived experience for persons with a high cervical spinal cord injury who have Electronic Aids to Daily Living (EADLs), and for persons who have no EADLs. Fifteen people with cervical spinal cord injuries attended four focus groups. Data analysis uncovered a novel framework of several themes that were organised into three categories: experiences, desires and meanings of living with EADL. Users’ and non users’ groups revealed homogenous themes. Experiences and desires are explored further in this paper. Themes within the category of experiences included: EADL devices, supply support and training, abandonment, mouthsticks and powered wheelchairs. Desires included: simple stuff, reliability, aesthetics and voice activation. Findings offer valuable personal insights about life with EADL to be considered by all involved with EADL.