Temporal decision making using unsupervised learning

With the explosion of ubiquitous continuous sensing, on-line streaming clustering continues to attract attention. The requirements are that the streaming clustering algorithm recognize and adapt clusters as the data evolves, that anomalies are detected, and that new clusters are automatically formed as incoming data dictate. In this dissertation, we develop a streaming clustering algorithm, MU Streaming Clustering (MUSC), that is based on coupling a Gaussian mixture model (GMM) with possibilistic clustering to build an adaptive system for analyzing streaming multi-dimensional activity feature vectors. For this reason, the possibilistic C-Means (PCM) and Automatic Merging Possibilistic Clustering Method (AMPCM) are combined together to cluster the initial data points, detect anomalies and initialize the GMM. MUSC achieves our goals when tested on synthetic and real-life datasets. We also compare MUSC's performance with Sequential k-means (sk-means), Basic Sequential Clustering Algorithm (BSAS), and Modified BSAS (MBSAS) here MUSC shows superiority in the performance and accuracy. The performance of a streaming clustering algorithm needs to be monitored over time to understand the behavior of the streaming data in terms of new emerging clusters and number of outlier data points. Incremental internal Validity Indices (iCVIs) are used to monitor the performance of an on-line clustering algorithm. We study the internal incremental Davies-Bouldin (DB), Xie-Beni (XB), and Dunn internal cluster validity indices in the context of streaming data analysis. We extend the original incremental DB (iDB) to a more general version parameterized by the exponent of membership weights. Then we illustrate how the iDB can be used to analyze and understand the performance of MUSC algorithm. We give examples that illustrate the appearance of a new cluster, the effect of different cluster sizes, handling of outlier data samples, and the effect of the input order on the resultant cluster history. In addition, we investigate the internal incremental Davies-Bouldin (iDB) cluster validity index in the context of big streaming data analysis. We analyze the effect of large numbers of samples on the values of the iCVI (iDB). We also develop online versions of two modified generalized Dunn's indices that can be used for dynamic evaluation of evolving (cluster) structure in streaming data. We argue that this method is a good way to monitor the ongoing performance of online clustering algorithms and we illustrate several types of inferences that can be drawn from such indices. We compare the two new indices to the incremental Xie-Beni and Davies-Bouldin indices, which to our knowledge offer the only comparable approach, with numerical examples on a variety of synthetic and real data sets. We also study the performance of MUSC and iCVIs with big streaming data applications. We show the advantage of iCVIs in monitoring large streaming datasets and in providing useful information about the data stream in terms of emergence of a new structure, amount of outlier data, size of the clusters, and order of data samples in each cluster. We also propose a way to project streaming data into a lower space for cases where the distance measure does not perform as expected in the high dimensional space. Another example of streaming is the data acivity data coming from TigerPlace and other elderly residents' apartments in and around Columbia. MO. TigerPlace is an eldercare facility that promotes aging-in-place in Columbia Missouri. Eldercare monitoring using non-wearable sensors is a candidate solution for improving care and reducing costs. Abnormal sensor patterns produced by certain resident behaviors could be linked to early signs of illness. We propose an unsupervised method for detecting abnormal behavior patterns based on a new context preserving representation of daily activities. A preliminary analysis of the method was conducted on data collected in TigerPlace. Sensor firings of each day are converted into sequences of daily activities. Then, building a histogram from the daily sequences of a resident, we generate a single data vector representing that day. Using the proposed method, a day with hundreds of sequences is converted into a single data point representing that day and preserving the context of the daily routine at the same time. We obtained an average Area Under the Curve (AUC) of 0.9 in detecting days where elder adults need to be assessed. Our approach outperforms other approaches on the same datset. Using the context preserving representation, we develoed a multi-dimensional alert system to improve the existing single-dimensional alert system in TigerPlace. Also, this represenation is used to develop a framework that utilizes sensor sequence similarity and medical concepts extracted from the EHR to automatically inform the nursing staff when health problems are detected. Our context preserving representation of daily activities is used to measure the similarity between the sensor sequences of different days. The medical concepts are extracted from the nursing notes using MetamapLite, an NLP tool included in the Unified Medical Language System (UMLS). The proposed idea is validated on two pilot datasets from twelve Tiger Place residents, with a total of 5810 sensor days out of which 1966 had nursing notes

Detection of visitors in elderly care using a low-resolution visual sensor network

Loneliness is a common condition associated with aging and comes with extreme health consequences including decline in physical and mental health, increased mortality and poor living conditions. Detecting and assisting lonely persons is therefore important-especially in the home environment. The current studies analyse the Activities of Daily Living (ADL) usually with the focus on persons living alone, e.g., to detect health deterioration. However, this type of data analysis relies on the assumption of a single person being analysed, and the ADL data analysis becomes less reliable without assessing socialization in seniors for health state assessment and intervention. In this paper, we propose a network of cheap low-resolution visual sensors for the detection of visitors. The visitor analysis starts by visual feature extraction based on foreground/background detection and morphological operations to track the motion patterns in each visual sensor. Then, we utilize the features of the visual sensors to build a Hidden Markov Model (HMM) for the actual detection. Finally, a rule-based classifier is used to compute the number and the duration of visits. We evaluate our framework on a real-life dataset of ten months. The results show a promising visit detection performance when compared to ground truth

4th. International Conference on Advanced Research Methods and Analytics (CARMA 2022)

Research methods in economics and social sciences are evolving with the increasing availability of Internet and Big Data sources of information. As these sources, methods, and applications become more interdisciplinary, the 4th International Conference on Advanced Research Methods and Analytics (CARMA) is a forum for researchers and practitioners to exchange ideas and advances on how emerging research methods and sources are applied to different fields of social sciences as well as to discuss current and future challenges. Due to the covid pandemic, CARMA 2022 is planned as a virtual and face-to-face conference, simultaneouslyDoménech I De Soria, J.; Vicente Cuervo, MR. (2022). 4th. International Conference on Advanced Research Methods and Analytics (CARMA 2022). Editorial Universitat Politècnica de València. https://doi.org/10.4995/CARMA2022.2022.1595

The basic assembly of skeletal models in the fall detection problem

The paper considers the appliance of the featureless approach to the human activity recognition problem, which exclude the direct anthropomorphic and visual characteristics of human figure from further analysis and thus increase the privacy of the monitoring system. A generalized pairwise comparison function of two human skeletal models, invariant to the sensor type, is used to project the object of interest to the secondary feature space, formed by the basic assembly of skeletons. A sequence of such projections in time forms an activity map, which allows an application of deep learning methods based on convolution neural networks for activity recognition. The proper ordering of skeletal models in a basic assembly plays an important role in secondary space design. The study of ordering of the basic assembly by the shortest unclosed path algorithm and correspondent activity maps for video streams from the TST Fall Detection v2 database are presented.The work was funded by the Ministry of Science and Higher Education of RF within the framework of the state task FEWG-2021-0012

Advances in automated surgery skills evaluation

Training a surgeon to be skilled and competent to perform a given surgical procedure, is an important step in providing a high quality of care and reducing the risk of complications. Traditional surgical training is carried out by expert surgeons who observe and assess the trainees directly during a given procedure. However, these traditional training methods are time-consuming, subjective, costly, and do not offer an overall surgical expertise evaluation criterion. The solution for these subjective evaluation methods is a sensor-based methodology able to objectively assess the surgeon's skill level. The development and advances in sensor technologies enable capturing and studying the information obtained from complex surgery procedures. If the surgical activities that occur during a procedure are captured using a set of sensors, then the skill evaluation methodology can be defined as a motion and time series analysis problem. This work aims at developing machine learning approaches for automated surgical skill assessment based on hand motion analysis. Specifically, this work presents several contributions to the field of objective surgical techniques using multi-dimensional time series, such as 1) introduce a new distance measure for the surgical activities based on the alignment of two multi-dimensional time series, 2) develop an automated classification framework to identify the surgeon proficiency level using wrist worn sensors, 3) develop a classification technique to identify elementary surgical tasks: suturing, needle passing, and knot tying , 4) introduce a new surgemes mean feature reduction technique which help improve the machine learning algorithms, 5) develop a framework for surgical gesture classification by employing the mean feature reduction method, 6) design an unsupervised method to identify the surgemes in a given procedure.Includes bibliographical references

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

Multi-dimensional Well-being Recognition System Using Daily Activity Data for Sustainable Living

芝浦工業大学2019年

Personal Constructs in Dementia Caregiving: The Family Caregiving Experience of People Living with Dementia in Saudi Arabia

As observed globally, family (informal and in-home) caregiving of older adults with Alzheimer’s disease or other forms of dementia has become a critical issue in the Arab region, including Saudi Arabia. This doctoral research psychometrically and conceptually evaluates an Arabic version of the Montgomery Borgatta Caregiver Burden Scale for use as a measurement tool to assess family caregivers of older adults living at home with dementia in Saudi Arabia. Currently, there is no published literature that addresses family caregiving for individuals with dementia in Saudi Arabia. Through further examination of family caregiving narratives, this research maps the personal and social construing of the family caregiver role of older adults with dementia in Saudi Arabia. This doctoral research is guided by the theoretical framework and philosophical understanding of personal construct theory and employs an integrated mixed methods approach to data collection, analysis, and interpretation of findings from 20 Saudi Arabian family caregivers. The research is presented in five chapters, including three individual manuscripts and introduction and conclusion chapters. The first manuscript introduces personal construct theory with its underlying philosophy, fundamental concepts, and methods of assessment as a potential constructivist research approach to examine the personal, familial, group, and cultural construct systems that shape the context of dementia care within and across cultures. The defined gap in the first manuscript led to a mixed methods study to examine the construction of Western-based existing measure of “caregiver burden.” The second manuscript, therefore, examines the items of the Montgomery Borgatta Caregiver Burden Scale and the construct of caregiver burden using the repertory grid technique and laddering procedure—the two constructivist methods derived from personal construct theory—to identify culturally sensitive items of the scale in the target cultural context of Saudi Arabia. Alongside the conceptual and psychometric evaluation of scale items, the third manuscript further examines family caregivers’ daily narratives and personal and cultural constructs that shape their caregiver role. This research contributes to the international literature of family gerontology and research on caregiver assessment. It elaborates the assessment methods of personal construct theory to expand alternatives for research methodologies of measurement evaluation and validation. The research also promotes the therapeutic approaches of personal construct theory and other practical implications for the development of support programs for family caregivers and recommends an integrated system for health and social services and a national strategy for dementia care in Saudi Arabia