MICAR: multi-inhabitant context-aware activity recognition in home environments

To examine the dependency levels of older adults living in nursing homes and home environments in their daily life activities, and the relationship involving pain, sleep, depression, and mental status affecting these levels. The comparative-descriptive and cross-sectional study was conducted in Cinarcik district of Yalova province, Turkey, from February to May 2016, and comprised older adults living in a nursing home and those in home environments. Data was collected using face-to-face interviews, a questionnaire, Barthel Index, Visual Analogue Scale, Standardised Mini-Mental State Examination, Pittsburg Sleep Quality Index and the Geriatric Depression Scale. Data was analysed using SPSS 20. Of the 185 subjects, 100(54%) were living in nursing homes and 85(46%) in home environments. Overall, there were 101(54.6%) women and 84(45.4%) men, and 97(52.4%) were aged >75 years. There was a significant positive relationship between Barthel Index and Standardised Mini Mental State Examination scores (p<0.05) and a significant negative relationship between Barthel Index and Geriatric Depression Scale scores among those living in nursing homes. Among those living in home environments, there was a statistically significant negative relationship involving Barthel Index, Visual Analogue Scale, Pittsburg Sleep Quality Index and the Geriatric Depression Scale scores (p<0.05). Also there was a significant positive relationship between Barthel Index and Standardised Mini- Mental State Examination scores (p<0.05). The dependency levels in daily life activities were found to be associated with mental status and depression in older adults living in nursing homes, while it was associated with pain, mental status, sleep and depression in those living in home environments.

Evaluation of a pushrim-activated, power-assisted wheelchair

Monitoring activities of daily living (ADL) is a frequent practice to determine the independence of elderly people in their homes instrumented with a set of sensors. To do so, the representation of ADL is an issue that must be addressed. In this paper, ADL representation based on task models is presented. To this extent, we propose an approach for task modelling of ADL by means of HAMSTERS-XL, an extensible task notation and tool allowing the design, visualisation, and simulation of task models. The proposed approach consists of several steps aiming to determine parameters, such as time and location, of both activities and sensors, as well as the events that need to be identified. To demonstrate the suitability of our approach, we present the description of two ADL performed by a resident within a smart home environment by employing the AGGIR grid variables, a tool adopted by the French government for determining the autonomy of an elderly individual. The obtained results are analyzed with the purpose of indicating the advantages of using ADL representation by means of task modeling.

Using ambient intelligence to assist people with dementia in carrying out their Activities of Daily Living (ADLs) independently in smart home environment is an important research area, due to the projected increasing number of people with dementia. We present herein, a system and algorithms for the automated recognition of ADLs; the ADLs are in terms of plans made up encoded sequences of micro-context information gathered by sensors in a smart home. Previously, the Erroneous-Plan Recognition (EPR) system was developed to specifically handle the wide spectrum of micro contexts from multiple sensing modalities. The EPR system monitors the person with dementia and determines if he has executed a correct or erroneous ADL. However, due to the noisy readings of the sensing modalities, the EPR system has problems in accurately detecting the erroneous ADLs. We propose to improve the accuracy of the EPR system by two new key components. First, we model the smart home environment as a Markov decision process (MDP), with the EPR system built upon it. Simple referencing of this model allows us to filter erroneous readings of the sensing modalities. Second, we use the reinforcement learning concept of probability and reward to infer erroneous readings that are not filtered by the first key component.We conducted extensive experiments and showed that the accuracy of the new EPR system is 26.2% higher than the previous system, and is therefore a better system for ambient assistive living applications.

Purpose As robots are not yet widely accepted in Europe as service providers in home environments, the authors intent to establish the possibility to distribute robotic elements and mechatronic devices in order to fuse them with various subsystems of buildings.Thus, robotic service systems could support Activities of Daily Living (ADLs), becoming an invisible part of the building.The proposed approach combines and applies strategies of performance oriented architecture 1 , ubiquitous computing 2 and service robotics 3 .Method Buildings and environments consist of a multitude of subsystems as for example walls, ceilings, furniture, service cores and many others.In order to realize the proposed vision of seamless robotic assistance, the authors, since 2008, extensively studied various subsystems, exploring how these could be fused with mechatronic and robotic elements.Currently, the research team enters Step 2 (Table 1).In the proposed paper, building subsystems that had been experimentally fused with mechatronic and robotic elements during Step 1 are described and evaluated.For each category, the authors present their implemented system, embedded into a 1:1 scale prototype experimental flat, which was developed for the realization of the proposed robotic environment.Additionally, the paper outlines: a) how the systems can support ADLs, b) architectural aspects, c) deployed sensor-actuator systems and d) the use of other basic technologies (Vision systems, Robot Operating System: ROS, etc.) during implementation. Results & DiscussionThe evaluations showed that the fusion of building subsystems with robotic technology, in each category (Wall, Ceiling, Service Core, Cabinet, Seat, In-House Mobility -Table 1), holds the potential to create assistive systems that are acceptable in the home environment, and moreover able to assist a multitude of ADLs.The evaluations also revealed that modularity and safety issues should be addressed intensively in future research.The author's ultimate goal is to explore more building subsystems and finally integrate all systems explored during step 1 and step 2 to an integrated robotic environment in step 3.

Humans differ from other animals in the way they can skilfully and precisely operate or invent tools to facilitate their everyday life. Tools have dominated our home, travel and work environment, becoming an integral step for our motor skills development. What happens when the part of the brain responsible for tool use is damaged in our adult life due to a cerebrovascular accident? How does daily life change when we lose the previously mastered ability to make use of the objects around us? How do patients suffering from compromised tool use cope with food preparation, personal hygiene, grooming, housework, or use of home appliances? In this literature review we present a state of the art for single and multiple tool use research, with a focus on the impact that apraxia (impaired ability to perform tool-based actions) and action disorganization syndrome (ADS; impaired ability to carry out multi-step actions) have on activities of daily living (ADL). Firstly, we summarize the behavioral studies investigating the impact of apraxia and other comorbidity syndromes, such as neglect or visual extinction, on ADL. We discuss the hallmarks of the compromised tool use in terms of the sequencing of action steps, conceptual errors committed, spatial motor control, and temporal organization of the movement. In addition, we present an up-to-date overview of the neuroimaging and lesion analyses studies that provide an insight into neural correlates of tool use in the human brain and functional changes in the neural organization following a stroke, in the context of ADL. Finally we discuss the current practice in neurorehabilitation of ADL in apraxia and ADS aiming at increasing patients’ independence.

The purpose of this study was to determine if persons with dementia of the Alzheimer type (DAT) differed from nondisabled older controls in the actual, experienced difficulty of specific process skills that affect performance of activities of daily living (ADL). A two-group comparison was performed and all participants were evaluated in either clinical or home environments to which they had been familiarized. Participants (341 persons with DAT and 287 controls) were selected from the standardization sample of the Assessment of Motor and Process Skills (AMPS) computer-scoring software. The demographic characteristics of the two groups were comparable. The AMPS was used to evaluate the effort, efficiency, safety, and independence of 20 ADL process skills that are compiled to enact performance of familiar ADL tasks. The raw data for each sample was subjected to many-faceted Rasch analysis to determine item difficulty calibrations of the ADL process skill items for each group. Actual item difficulty calibrations of 19 ADL process skills differed meaningfully between the two groups. The results indicated that it is possible to identify specific ADL process skills that are actually easier or more difficult for each group. In addition, the results supported earlier research that has demonstrated that the underlying cognitive and physical impairments demonstrated by persons with DAT do manifest as ADL disability, negatively impacting their ability to perform ADL tasks.

In the United States, the incidence rate of new or recurrent stroke is approximately 795 000 per year, and stroke prevalence for individuals over the age of 20 years is estimated at 6.5 million.1 Mortality rates in the first 30 days after stroke have decreased because of advances in emergency medicine and acute stroke care. In addition, there is strong evidence that organized postacute, inpatient stroke care delivered within the first 4 weeks by an interdisciplinary healthcare team results in an absolute reduction in the number of deaths.2,3 Despite these positive achievements, stroke continues to represent the leading cause of long-term disability in Americans: An estimated 50 million stroke survivors worldwide currently cope with significant physical, cognitive, and emotional deficits, and 25% to 74% of these survivors require some assistance or are fully dependent on caregivers for activities of daily living (ADLs).4,5 Notwithstanding the substantial progress in acute stroke care over the past 15 years, the focus of stroke medical advances and healthcare resources has been on acute and subacute recovery phases, which has resulted in substantial health disparities in later phases of stroke care. Additionally, healthcare providers (HCPs) are often unaware of not only patients’ potential for improvement during more chronic recovery phases but also common issues that stroke survivors and their caregivers experience. Furthermore, even with evidence that documents neuroplasticity potential regardless of age and time after stroke,6 the mean lifetime cost of ischemic stroke (which accounts for 87% of all strokes) in the United States is an estimated $140 000 (for inpatient, rehabilitation, and follow-up costs), with 70% of first-year stroke costs attributed to acute inpatient hospital care1; therefore, fewer financial resources appear to be dedicated to providing optimal care during the later phases of stroke recovery. Because there remains a …

Automatic detection and recognition of Activities of Daily Living (ADL) are crucial for providing effective care to frail older adults living alone. A step forward in addressing this challenge is the deployment of smart home sensors capturing the intrinsic nature of ADLs performed by these people. As the real-life scenario is characterized by a comprehensive range of ADLs and smart home layouts, deviations are expected in the number of sensor events per activity (SEPA), a variable often used for training activity recognition models. Such models, however, rely on the availability of suitable and representative data collection and is habitually expensive and resource-intensive. Simulation tools are an alternative for tackling these barriers; nonetheless, an ongoing challenge is their ability to generate synthetic data representing the real SEPA. Hence, this paper proposes the use of Poisson regression modelling for transforming simulated data in a better approximation of real SEPA. First, synthetic and real data were compared to verify the equivalence hypothesis. Then, several Poisson regression models were formulated for estimating real SEPA using simulated data. The outcomes revealed that real SEPA can be better approximated ( R pred 2 = 92.72 % ) if synthetic data is post-processed through Poisson regression incorporating dummy variables.

Eye movements are essential in comprehending the environment. Users’ attention may be directed via eye gazing, which can enhance Human–Computer Interaction (HCI). Gaze estimation can make HCI more natural in a non-intrusive way. For example, tiredness detection, biometric identification, illness diagnosis, activity recognition, an estimate of alertness level and gaze-contingent display are all possible uses of eye-tracking. It's been available for decades, but it's not been widely used in consumer applications. The high cost of eye-tracking technology and the lack of consumer-level applications are the main reasons behind this. When it comes to Activities of Daily Living (ADL), assistive robots may restore essential levels of independence for the elderly and others who are disabled. Although people can communicate their wishes in numerous ways, linguistic patterns and gaze-based implicit communication of intentions remain underdeveloped, such as bodily expressions or actions. In this study, based on the eye view, a new, implicit, nonverbal communication paradigm for HCI is introduced. Conventional gaze detection systems rely on infrared lights and cameras with high-resolution sensors to achieve outstanding performance and durability. But these systems need to be modified. Thus they are confined to laboratory research and challenging to apply in the real world. Here, we recommend that the gaze be followed using a webcam. Using a webcam to obtain 2D coordinates, we provide a practical visual monitoring framework based on models. This work on the platform is intended to ease HCI and thus increase useful technology and consumers’ privacy in their daily lives. The test results of this experiment indicated that implicit human gaze patterns on visualized objects could efficiently be used for contact with people's intentions. Studies have also shown that it is simple to comprehend and use contact with the gaze. It is further suggested that the subject-dependent eye parameters are calculated using a specific reference system. Finally, an implicit communication system for monitoring and interpreting purposes has been developed. After understanding the user's implicit intention to support the elderly with the action of the eye-gaze, the device can distinguish the activities/requirements required from the home environment. Finally, to direct caregivers to deliver the correct service, the implied purpose may then be used.KeywordsActivities of daily living (ADL)Eye-gaze trackingGaze-based communicationHuman–computer interaction (HCI)Implicit intention inferenceWebcam

Human activity recognition (HAR) is used to support older adults to live independently in their own homes. Once activities of daily living (ADL) are recognised, gathered information will be used to identify abnormalities in comparison with the routine activities. Ambient sensors, including occupancy sensors and door entry sensors, are often used to monitor and identify different activities. Most of the current research in HAR focuses on a single-occupant environment when only one person is monitored, and their activities are categorised. The assumption that home environments are occupied by one person all the time is often not true. It is common for a resident to receive visits from family members or health care workers, representing a multi-occupancy environment. Entropy analysis is an established method for irregularity detection in many applications; however, it has been rarely applied in the context of ADL and HAR. In this paper, a novel method based on different entropy measures, including Shannon Entropy, Permutation Entropy, and Multiscale-Permutation Entropy, is employed to investigate the effectiveness of these entropy measures in identifying visitors in a home environment. This research aims to investigate whether entropy measures can be utilised to identify a visitor in a home environment, solely based on the information collected from motion detectors [e.g., passive infra-red] and door entry sensors. The entropy measures are tested and evaluated based on a dataset gathered from a real home environment. Experimental results are presented to show the effectiveness of entropy measures to identify visitors and the time of their visits without the need for employing extra wearable sensors to tag the visitors. The results obtained from the experiments show that the proposed entropy measures could be used to detect and identify a visitor in a home environment with a high degree of accuracy.

With the realisation of the Internet of Things (IoT) paradigm, the analysis of the Activities of Daily Living (ADLs), in a smart home environment, is becoming an active research domain. The existence of representative datasets is a key requirement to advance the research in smart home design. Such datasets are an integral part of the visualisation of new smart home concepts as well as the validation and evaluation of emerging machine learning models. Machine learning techniques that can learn ADLs from sensor readings are used to classify, predict and detect anomalous patterns. Such techniques require data that represent relevant smart home scenarios, for training, testing and validation. However, the development of such machine learning techniques is limited by the lack of real smart home datasets, due to the excessive cost of building real smart homes. This paper provides two datasets for classification and anomaly detection. The datasets are generated using OpenSHS, (Open Smart Home Simulator), which is a simulation software for dataset generation. OpenSHS records the daily activities of a participant within a virtual environment. Seven participants simulated their ADLs for different contexts, e.g., weekdays, weekends, mornings and evenings. Eighty-four files in total were generated, representing approximately 63 days worth of activities. Forty-two files of classification of ADLs were simulated in the classification dataset and the other forty-two files are for anomaly detection problems in which anomalous patterns were simulated and injected into the anomaly detection dataset.

Activity modelling plays a critical role in activity recognition and assistance in smart home based assisted living. Ontology-based activity modelling is able to leverage domain knowledge and heuristics to create Activities of Daily Living (ADL) models with rich semantics. However, they suffer from incompleteness, inflexibility, and lack of adaptation. In this paper, we propose a novel approach for learning and evolving activity models. The approach uses predefined ADL ontologies to identify activities from sensor activation streams. We develop algorithms that analyze logs of activity data to discover new activities as well as the conditions for evolving the seed ADL ontologies. We illustrate our approach through a scenario that shows how ADL models can be evolved to accommodate new ADL activities and preferences of individual smart home's inhabitants.

FLAAP: An Open Human Activity Recognition (HAR) Dataset for Learning and Finding the Associated Activity Patterns

Food allergies (FAs) have far-reaching implications regarding occupational participation. Children with food allergies and their families experience impacts on their daily life activities. The current standard of care for FAs fails to adequately address the wide-ranging barriers to participation in home, school, and community environments. This study used a mixed-method design to examine the impacts of FA on the needs of children and families affected by FA. Eighteen parents of children with FAs and six health care providers of children with FAs were interviewed; the parents also completed two questionnaires to identify the level of impact of FA on areas of daily living. The results indicated that parents perceive FA as a significant barrier to several aspects of daily life, and parents and health care providers recognized the need for additional supports for families of children with FA. Occupational therapists can intervene to help families with FA improve their participation in daily life activities while managing and limiting exposure to FAs. Expanding the role of occupational therapists to address the needs of families with FAs and recommendations for supporting this population are discussed.