Jit Biswas

An ontology-based context model in monitoring and handling agitation behavior for persons with dementia

This paper presents a new approach that exploits semantic Web standards to provide a reusable middleware support for flexible event representation, query and reasoning, and standardized schemes for automated intervention triggering and activity planning to handle agitation detected in persons with dementia. The proposed context model enables the development of sophisticated systems that facilitates caregiving and clinical assessment of dementia patients in a context enlightened fashion. In particular, we describe the use of the Web ontology language (OWL) to model agitation quantification using scale to assess observed agitation in persons with dementia of the Alzheimer type (SOAPD), intervention and social contexts to build an integrated monitoring and intervention system that exceeds the current state-of-the-art in robustness, intelligence and scalability

Deployment of assistive living technology in a nursing home environment: methods and lessons learned

BackgroundWith an ever-growing ageing population, dementia is fast becoming the chronic disease of the 21st century. Elderly people affected with dementia progressively lose their autonomy as they encounter problems in their Activities of Daily Living (ADLs). Hence, they need supervision and assistance from their family members or professional caregivers, which can often lead to underestimated psychological and financial stress for all parties. The use of Ambient Assistive Living (AAL) technologies aims to empower people with dementia and relieve the burden of their caregivers.The aim of this paper is to present the approach we have adopted to develop and deploy a system for ambient assistive living in an operating nursing home, and evaluate its performance and usability in real conditions. Based on this approach, we emphasise on the importance of deployments in real world settings as opposed to prototype testing in laboratories.MethodsWe chose to conduct this work in close partnership with end-users (dementia patients) and specialists in dementia care (professional caregivers). Our trial was conducted during a period of 14 months within three rooms in a nursing home in Singapore, and with the participation of eight dementia patients and two caregivers. A technical ambient assistive living solution, consisting of a set of sensors and devices controlled by a software platform, was deployed in the collaborating nursing home. The trial was preceded by a pre-deployment period to organise several observation sessions with dementia patients and focus group discussions with professional caregivers. A process of ground truth and system’s log data gathering was also planned prior to the trial and a system performance evaluation was realised during the deployment period with the help of caregivers. An ethical approval was obtained prior to real life deployment of our solution.ResultsPatients’ observations and discussions allowed us to gather a set of requirements that a system for elders with mild-dementia should fulfil. In fact, our deployment has exposed more concrete requirements and problems that need to be addressed, and which cannot be identified in laboratory testing. Issues that were neither forecasted during the design phase nor during the laboratory testing surfaced during deployment, thus affecting the effectiveness of the proposed solution. Results of the system performance evaluation show the evolution of system precision and uptime over the deployment phases, while data analysis demonstrates the ability to provide early detection of the degradation of patients’ conditions. A qualitative feedback was collected from caregivers and doctors and a set of lessons learned emerged from this deployment experience. (Continued on next page) (Continued from previous page)ConclusionLessons learned from this study were very useful for our research work and can serve as inspiration for developers and providers of assistive living services. They confirmed the importance of real deployment to evaluate assistive solutions especially with the involvement of professional caregivers. They also asserted the need for larger deployments. Larger deployments will allow to conduct surveys on assistive solutions social and health impact, even though they are time and manpower consuming during their first phases.

Analysis and comparison of sleeping posture classification methods using pressure sensitive bed system

Pressure ulcers are common problems for bedridden patients. Caregivers need to reposition the sleeping posture of a patient every two hours in order to reduce the risk of getting ulcers. This study presents the use of Kurtosis and skewness estimation, principal component analysis (PCA) and support vector machines (SVMs) for sleeping posture classification using cost-effective pressure sensitive mattress that can help caregivers to make correct sleeping posture changes for the prevention of pressure ulcers.

A Possibilistic Approach for Activity Recognition in Smart Homes for Cognitive Assistance to Alzheimer’s Patients

Providing cognitive assistance to Alzheimer’s patients in smart homes is a field of research that receives a lot of attention lately. The recognition of the patient’s behavior when he carries out some activities in a smart home is primordial in order to give adequate assistance at the opportune moment. To address this challenging issue, we present a formal activity recognition framework based on possibility theory and description logics. We present initial results from an implementation of this recognition approach in a smart home laboratory.

Processing of wearable sensor data on the cloud - a step towards scaling of continuous monitoring of health and well-being

As part of a sleep monitoring project, we used actigraphy based on body-worn accelerometer sensors to remotely monitor and study the sleep-wake cycle of elderly staying at nursing homes. We have conducted a fifteen patient trial of a sleep activity pattern monitoring (SAPM) system at a local nursing home. The data was collected and stored in our server and the processing of the data was done offline after sleep diaries used for validation and ground truth were updated into the system. The processing algorithm matches and annotates the sensor data with manual sleep diary information and is processed asynchronously on the grid/cloud back end. In this paper we outline the mapping of the system for grid / cloud processing, and initial results that show expected near-linear performance for scaling the number of users.

Eating activity primitives detection - a step towards ADL recognition

Activity of daily living (ADL) monitoring is important in order to determine the well being of elderly persons in their home settings. One important question is, ldquoIs the elderly person able to eat properly on his own?rdquo In this paper we present some results of our preliminary work on an algorithm for detection of the eating activity. The algorithm uses a dynamic Bayesian network based approach to reduce the complexity of determining states. Initial results are quite promising and point to a general algorithmic approach that a) uses multiple modalities of sensors for gathering data, b) detects activity primitives and c) stores detected activity primitives as micro-context for future use.

Moving targets detection and localization in passive infrared sensor networks

This paper presents the method for the detection and localization of moving targets in passive infrared (PIR) sensor networks in both indoor and outdoor settings. It reports our design and implementation of PIR sensor network, especially, we proposed a detection algorithm, which uses adaptive threshold with constant false alarm rate; and developed a localization algorithm using direction search in the grid space of the network. The experimental results have shown that our PIR sensor network can detect and locate the moving targets with reasonable accuracy.

New trends to support independence in persons with mild dementia: a mini-review.

Our research was motivated by the growing aging population worldwide and the need to concentrate research efforts on a specific target group; it focuses on elderly persons with physical and cognitive deficiencies. The primary goal is to enable persons with mild dementia to maximize their physical and mental functions through assistive technologies in order to be able to continue to participate in social networks and lead independent and purposeful lives. Persons with mild dementia usually have problems in performing activities of daily living due to episodic memory decline. These can include simple activities, such as bathing, changing clothes and preparing meals. Through extended field test trials involving end users, we have demonstrated that assistive technology that provides timely prompts, alarms and reminders can enable them to preserve their abilities and improve their quality of life. Understanding the user context, especially when targeting demented individuals, and providing the required personalized assistive services is the objective of our research work. Finding the appropriate user interface to interact with the provided services is often a barrier. Thus, we have adopted the approach of a multimodal interactive system with the living environment including a TV set, iPad-like tablets, sensors/actuators, and wireless speakers connected to a reasoning engine that is able to consider the complexity of the users’ profile defined by his/her cognitive abilities. In this paper we will mainly focus on the interaction level with the system as well as on the validation stages performed to meet the users’ requirements. This is the result of several years’ work since 2006 in the frame of two projects (IST-FP6 COGKNOW European completed project and AMUPADH ongoing project in Singapore).

Scream detection for home applications

Audio signal is an important clue for the situation awareness. It provides complementary information for video signal. For home care, elder care, and security application, screaming is one of the events people (family member, care giver, and security guard) are especially interested in. We present here an approach to scream detection, using both analytic and statistical features for the classification. In audio features, sound energy is a useful feature to detect scream like audio. We adopt the log energy to detect the energy continuity of the audio to represent the screaming which is often lasting longer than many other sounds. Further, a robust high pitch detection based on the autocorrelation is presented to extract the highest pitch for each frame, followed by a pitch analysis for a time window containing multiple frames. Finally, to validate the scream like sound, a SVM based classifier is applied with the feature vector generated from the MFCCs across a window of frames. Experiments of screaming detection is discussed with promising results shown. The algorithm is ported and run in a Linux based set top box connected by a microphone array to capture the audio for live scream detection.

Health and wellness monitoring through wearable and ambient sensors: exemplars from home-based care of elderly with mild dementia

Monitoring and timely intervention are extremely important in the continuous management of health and wellness among all segments of the population, but particularly among those with mild dementia. In relation to this, we prescribe three design principles for the construction of services and applications. These are ambient intelligence, service continuity, and micro-context. In this paper, we provide three exemplars from our research and development activities that illustrate the use of these design principles in the construction of services and applications. All the applications are drawn from the field of care for mild dementia patients in their living quarters.