Unsupervised monitoring of an elderly person\u27s activities of daily living using Kinect sensors and a power meter

The need for greater independence amongst the growing population of elderly people has made the concept of “ageing in place” an important area of research. Remote home monitoring strategies help the elderly deal with challenges involved in ageing in place and performing the activities of daily living (ADLs) independently. These monitoring approaches typically involve the use of several sensors, attached to the environment or person, in order to acquire data about the ADLs of the occupant being monitored. Some key drawbacks associated with many of the ADL monitoring approaches proposed for the elderly living alone need to be addressed. These include the need to label a training dataset of activities, use wearable devices or equip the house with many sensors. These approaches are also unable to concurrently monitor physical ADLs to detect emergency situations, such as falls, and instrumental ADLs to detect deviations from the daily routine. These are all indicative of deteriorating health in the elderly. To address these drawbacks, this research aimed to investigate the feasibility of unsupervised monitoring of both physical and instrumental ADLs of elderly people living alone via inexpensive minimally intrusive sensors. A hybrid framework was presented which combined two approaches for monitoring an elderly occupant’s physical and instrumental ADLs. Both approaches were trained based on unlabelled sensor data from the occupant’s normal behaviours. The data related to physical ADLs were captured from Kinect sensors and those related to instrumental ADLs were obtained using a combination of Kinect sensors and a power meter. Kinect sensors were employed in functional areas of the monitored environment to capture the occupant’s locations and 3D structures of their physical activities. The power meter measured the power consumption of home electrical appliances (HEAs) from the electricity panel. A novel unsupervised fuzzy approach was presented to monitor physical ADLs based on depth maps obtained from Kinect sensors. Epochs of activities associated with each monitored location were automatically identified, and the occupant’s behaviour patterns during each epoch were represented through the combinations of fuzzy attributes. A novel membership function generation technique was presented to elicit membership functions for attributes by analysing the data distribution of attributes while excluding noise and outliers in the data. The occupant’s behaviour patterns during each epoch of activity were then classified into frequent and infrequent categories using a data mining technique. Fuzzy rules were learned to model frequent behaviour patterns. An alarm was raised when the occupant’s behaviour in new data was recognised as frequent with a longer than usual duration or infrequent with a duration exceeding a data-driven value. Another novel unsupervised fuzzy approach to monitor instrumental ADLs took unlabelled training data from Kinect sensors and a power meter to model the key features of instrumental ADLs. Instrumental ADLs in the training dataset were identified based on associating the occupant’s locations with specific power signatures on the power line. A set of fuzzy rules was then developed to model the frequency and regularity of the instrumental activities tailored to the occupant. This set was subsequently used to monitor new data and to generate reports on deviations from normal behaviour patterns. As a proof of concept, the proposed monitoring approaches were evaluated using a dataset collected from a real-life setting. An evaluation of the results verified the high accuracy of the proposed technique to identify the epochs of activities over alternative techniques. The approach adopted for monitoring physical ADLs was found to improve elderly monitoring. It generated fuzzy rules that could represent the person’s physical ADLs and exclude noise and outliers in the data more efficiently than alternative approaches. The performance of different membership function generation techniques was compared. The fuzzy rule set obtained from the output of the proposed technique could accurately classify more scenarios of normal and abnormal behaviours. The approach for monitoring instrumental ADLs was also found to reliably distinguish power signatures generated automatically by self-regulated devices from those generated as a result of an elderly person’s instrumental ADLs. The evaluations also showed the effectiveness of the approach in correctly identifying elderly people’s interactions with specific HEAs and tracking simulated upward and downward deviations from normal behaviours. The fuzzy inference system in this approach was found to be robust in regards to errors when identifying instrumental ADLs as it could effectively classify normal and abnormal behaviour patterns despite errors in the list of the used HEAs

Behaviour Profiling using Wearable Sensors for Pervasive Healthcare

In recent years, sensor technology has advanced in terms of hardware sophistication and miniaturisation. This has led to the incorporation of unobtrusive, low-power sensors into networks centred on human participants, called Body Sensor Networks. Amongst the most important applications of these networks is their use in healthcare and healthy living. The technology has the possibility of decreasing burden on the healthcare systems by providing care at home, enabling early detection of symptoms, monitoring recovery remotely, and avoiding serious chronic illnesses by promoting healthy living through objective feedback. In this thesis, machine learning and data mining techniques are developed to estimate medically relevant parameters from a participant‘s activity and behaviour parameters, derived from simple, body-worn sensors. The first abstraction from raw sensor data is the recognition and analysis of activity. Machine learning analysis is applied to a study of activity profiling to detect impaired limb and torso mobility. One of the advances in this thesis to activity recognition research is in the application of machine learning to the analysis of 'transitional activities': transient activity that occurs as people change their activity. A framework is proposed for the detection and analysis of transitional activities. To demonstrate the utility of transition analysis, we apply the algorithms to a study of participants undergoing and recovering from surgery. We demonstrate that it is possible to see meaningful changes in the transitional activity as the participants recover. Assuming long-term monitoring, we expect a large historical database of activity to quickly accumulate. We develop algorithms to mine temporal associations to activity patterns. This gives an outline of the user‘s routine. Methods for visual and quantitative analysis of routine using this summary data structure are proposed and validated. The activity and routine mining methodologies developed for specialised sensors are adapted to a smartphone application, enabling large-scale use. Validation of the algorithms is performed using datasets collected in laboratory settings, and free living scenarios. Finally, future research directions and potential improvements to the techniques developed in this thesis are outlined

Discovering human activities from binary data in smart homes

With the rapid development in sensing technology, data mining, and machine learning fields for human health monitoring, it became possible to enable monitoring of personal motion and vital signs in a manner that minimizes the disruption of an individual’s daily routine and assist individuals with difficulties to live independently at home. A primary difficulty that researchers confront is acquiring an adequate amount of labeled data for model training and validation purposes. Therefore, activity discovery handles the problem that activity labels are not available using approaches based on sequence mining and clustering. In this paper, we introduce an unsupervised method for discovering activities from a network of motion detectors in a smart home setting. First, we present an intra-day clustering algorithm to find frequent sequential patterns within a day. As a second step, we present an inter-day clustering algorithm to find the common frequent patterns between days. Furthermore, we refine the patterns to have more compressed and defined cluster characterizations. Finally, we track the occurrences of various regular routines to monitor the functional health in an individual’s patterns and lifestyle. We evaluate our methods on two public data sets captured in real-life settings from two apartments during seven-month and three-month periods

Using topic models to detect behaviour patterns for healthcare monitoring

Healthcare systems worldwide are facing growing demands on their resources due to an ageing population and increase in prevalence of chronic diseases. Innovative residential healthcare monitoring systems, using a variety of sensors are being developed to help address these needs. Interpreting the vast wealth of data generated is key to fully exploiting the benefits offered by a monitoring system. This thesis presents the application of topic models, a machine learning algorithm, to detect behaviour patterns in different types of data produced by a monitoring system. Latent Dirichlet Allocation was applied to real world activity data with corresponding ground truth labels of daily routines. The results from an existing dataset and a novel dataset collected using a custom mobile phone app, demonstrated that the patterns found are equivalent of routines. Long term monitoring can identify changes that could indicate an alteration in health status. Dynamic topic models were applied to simulated long term activity datasets to detect changes in the structure of daily routines. It was shown that the changes occurring in the simulated data can successfully be detected. This result suggests potential for dynamic topic models to identify changes in routines that could aid early diagnosis of chronic diseases. Furthermore, chronic conditions, such as diabetes and obesity, are related to quality of diet. Current research findings on the association between eating behaviours, especially snacking, and the impact on diet quality and health are often conflicting. One problem is the lack of consistent definitions for different types of eating event. The novel application of Latent Dirichlet Allocation to three nutrition datasets is described. The results demonstrated that combinations of food groups representative of eating event types can be detected. Moreover, labels assigned to these combinations showed good agreement with alternative methods for labelling eating event types

Entropy measures for anomaly detection

Human activity recognition methods are used to support older adults to live independently in their own homes by monitoring their Activities of Daily Living (ADL). The gathered data and information representing different activities will be used to identify anomalous activities in comparison with the routine activities. In the related research in this area, the most recent studies have mainly focused on detecting anomalies in a single occupant environment. Although older adults often receive visits from family members or health care workers, representing a multi-occupancy environment. This research is focused on the application of entropy measures for anomaly detection in ADLs in a single-occupancy and multi-occupancy environment. In many applications, entropy measures are used to detect the irregularities and the degree of randomness in data. However, this has rarely been applied in the context of activities of daily living. To address the research questions identified in the thesis, three novel contributions of the thesis are; Firstly, a novel method based on different entropy measures is investigated to detect anomalies in ADLs, specifically in sleeping routine and human falls. Secondly, a novel entropy-based method is explored to detect anomalies in ADLs in the presence of a visitor, solely based on information gathered from ambient sensors. Finally, entropy measures are applied to investigate their effectiveness in identifying a visitor in a single home environment based on data gathered from ambient sensors. The results presented in this thesis show that entropy measures could be used to detect abnormality (here, irregular sleep, human fall and a visitor) in ADLs

A Learning Health Sciences Approach to Understanding Clinical Documentation in Pediatric Rehabilitation Settings

The work presented in this dissertation provides an analysis of clinical documentation that challenges the concepts and thinking surrounding missingness of data from clinical settings and the factors that influence why data are missing. It also foregrounds the critical role of clinical documentation as infrastructure for creating learning health systems (LHS) for pediatric rehabilitation settings. Although completeness of discrete data is limited, the results presented do not reflect the quality of care or the extent of unstructured data that providers document in other locations of the electronic health record (EHR) interface. While some may view imputation and natural language processing as means to address missingness of clinical data, these practices carry biases in their interpretations and issues of validity in results. The factors that influence missingness of discrete clinical data are rooted not just in technical structures, but larger professional, system level and unobservable phenomena that shape provider practices of clinical documentation. This work has implications for how we view clinical documentation as critical infrastructure for LHS, future studies of data quality and health outcomes research, and EHR design and implementation. The overall research questions for this dissertation are: 1) To what extent can data networks be leveraged to build classifiers of patient functional performance and physical disability? 2) How can discrete clinical data on gross motor function be used to draw conclusions about clinical documentation practices in the EHR for cerebral palsy? 3) Why does missingness of discrete data in the EHR occur? To address these questions, a three-pronged approach is used to examine data completeness and the factors that influence missingness of discrete clinical data in an exemplar pediatric data learning network will be used. As a use-case, evaluation of EHR data completeness of gross motor function related data, populated by providers from 2015-2019 for children with cerebral palsy (CP), will be completed. Mixed methods research strategies will be used to achieve the dissertation objectives, including developing an expert-informed and standards-based phenotype model of gross motor function data as a task-based mechanism, conducting quantitative descriptive analyses of completeness of discrete data in the EHR, and performing qualitative thematic analyses to elicit and interpret the latent concepts that contribute to missingness of discrete data in the EHR. The clinical data for this dissertation are sourced from the Shriners Hospitals for Children (SHC) Health Outcomes Network (SHOnet), while qualitative data were collected through interviews and field observations of clinical providers across three care sites in the SHC system.PHDHlth Infrastr & Lrng Systs PhDUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162994/1/njkoscie_1.pd

Wearable Sensors in the Evaluation of Gait and Balance in Neurological Disorders

The aging population and the increased prevalence of neurological diseases have raised the issue of gait and balance disorders as a major public concern worldwide. Indeed, gait and balance disorders are responsible for a high healthcare and economic burden on society, thus, requiring new solutions to prevent harmful consequences. Recently, wearable sensors have provided new challenges and opportunities to address this issue through innovative diagnostic and therapeutic strategies. Accordingly, the book “Wearable Sensors in the Evaluation of Gait and Balance in Neurological Disorders” collects the most up-to-date information about the objective evaluation of gait and balance disorders, by means of wearable biosensors, in patients with various types of neurological diseases, including Parkinson’s disease, multiple sclerosis, stroke, traumatic brain injury, and cerebellar ataxia. By adopting wearable technologies, the sixteen original research articles and reviews included in this book offer an updated overview of the most recent approaches for the objective evaluation of gait and balance disorders

Locomotion Traces Data Mining for Supporting Frail People with Cognitive Impairment

The rapid increase in the senior population is posing serious challenges to national healthcare systems. Hence, innovative tools are needed to early detect health issues, including cognitive decline. Several clinical studies show that it is possible to identify cognitive impairment based on the locomotion patterns of older people. Thus, this thesis at first focused on providing a systematic literature review of locomotion data mining systems for supporting Neuro-Degenerative Diseases (NDD) diagnosis, identifying locomotion anomaly indicators and movement patterns for discovering low-level locomotion indicators, sensor data acquisition, and processing methods, as well as NDD detection algorithms considering their pros and cons. Then, we investigated the use of sensor data and Deep Learning (DL) to recognize abnormal movement patterns in instrumented smart-homes. In order to get rid of the noise introduced by indoor constraints and activity execution, we introduced novel visual feature extraction methods for locomotion data. Our solutions rely on locomotion traces segmentation, image-based extraction of salient features from locomotion segments, and vision-based DL. Furthermore, we proposed a data augmentation strategy to increase the volume of collected data and generalize the solution to different smart-homes with different layouts. We carried out extensive experiments with a large real-world dataset acquired in a smart-home test-bed from older people, including people with cognitive diseases. Experimental comparisons show that our system outperforms state-of-the-art methods

Cultural Diffusion through Language: How Communication Networks Influence Culture in the Age of Digitization

My dissertation focuses on the strategic implications of the link between organizational culture and social network structure. I study their role in the process of knowledge transfer and diffusion, organizational memory, and organizational design. More broadly, I examine the way that social structure influences the information environment, and what effect this has on organizational learning. I focus in particular on the process of cultural evolution. My dissertation leverages digitization as a phenomenon of inherent interest and as an empirical setting that can improve our theoretical understanding of both digital and non-digital communities. I have developed an expertise in computational methods, especially in machine learning techniques related to text and other unstructured data, and in the analysis of "big data," especially pertaining to large-scale networks. By combining these computational tools with organizational theory and the rich relational data generated by the explosion of digital records, my research grants insight into the dynamic process of learning in organizations and the implications for innovation and competitive advantage. I explore how digitization informs and develops our understanding of organizational culture, knowledge transfer, and the labor market. Specifically, I investigate how digitization has opened a window to observe network structure and language, providing a lasting record of these changes through time. Using these digital records to observe the structure of social relations and the language used to communicate can help deepen our theory of knowledge transfer for a wide range of organizations, not just those that operate in the digital sphere. This means that these studies also have implications for understanding organizations in non-digital settings. My dissertation contributes both theoretically and empirically to the knowledge theory of the firm. However, the mechanisms underlying knowledge transfer remain underdeveloped. I contribute by disentangling the related mechanisms of language and organizational structure, and I propose that common language directly impacts what knowledge may be efficiently transferred. Next, my dissertation contributes to the growing field of digitization. Digitization is salient for researchers both as a unique phenomenon and as an ever-expanding source of accessible data to test theory. Moreover, since one of the central contributions of digitization is to reduce the cost of information gathering, it is well-suited to my theoretical setting of knowledge transmission and organizational memory. Finally, my dissertation contributes to our understanding of culture in organizations. The focus on language as an aspect of culture allows both additional formalization as well as more specific empirical tests of the contribution of culture to organizational outcomes. In particular, a focus on dynamic settings in each of the chapters reveals the interplay between organizational structure, memory, and change. This helps us to understand how language evolves, how it is learned, and how it changes in response to information shocks