Andrei Tolstikov

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.

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.

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.

2-layer Erroneous-Plan Recognition for dementia patients in smart homes

People with dementia lose their ability to learn, solve problems, and communicate. And they are all around us. To potentially replace some of their diminished memory and problem-solving abilities, Erroneous-Plan Recognition (EPR) aims to detect defects or faults in the execution of correct plans by the dementia patient, and send timely audio and visual prompts to the dementia patient and caregiver in order to correct these faults. The scope of this work is for the patient who lives alone in a smart home. One challenge is that the definition of plan can be very subjective. It is necessary to regard a plan as an Activity of Daily Living (ADL), choose the ADLs to monitor, and deploy available sensors to acquire data. With the sensor data, there can be activity recognition, followed by plan recognition. Another challenge is the highly random and erroneous behaviour of dementia patients. Multiple, sequential, and independent layers of error detection can be arranged in a prioritised manner to detect specific errors first, and provide an error probability if no specific errors are detected. On the whole, most of the EPR results are very good as they are at least 0.9, indicating that the data is linearly separable. The 2-layer EPR system, which uses the blacklist and whitelist as Layer 1 and naive Bayes classifier as Layer 2, is significantly more accurate than each individual layer. In fact, 5 out of 6 actors have an accuracy above 0.9. With the encouraging results, there will be more technical and domain challenges which we can address in the near future.

Improving the accuracy of erroneous-plan recognition system for Activities of Daily Living

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.

Information Quality Management in Sensor Networks based on the Dynamic Bayesian Network model

To satisfy application information quality (IQ) constraints in a sensor network, the efficient way is to choose the most appropriate sensor nodes and sensor modalities which would provide a required IQ for the current state of the system. In this paper, two formulations of an activity recognition application are considered - the first based on static Bayesian network (BN), and the second on dynamic Bayesian network (DBN) which allows temporal changes to the conditional probabilities of the system states. It is shown that for similar results, in the certainty of state estimation, the formulation based on DBN uses much less resources, because it relies significantly on the readings obtained in the past. Also DBN model is more robust since it greatly reduces the likelihood of selecting unnaturally drastic state changes.

Model and algorithmic framework for detection and correction of cognitive errors

This paper outlines an approach that we are taking for elder-care applications in the smart home, involving cognitive errors and their compensation. Our approach involves high level modeling of daily activities of the elderly by breaking down these activities into smaller units, which can then be automatically recognized at a low level by collections of sensors placed in the homes of the elderly. This separation allows us to employ plan recognition algorithms and systems at a high level, while developing stand-alone activity recognition algorithms and systems at a low level. It also allows the mixing and matching of multi-modality sensors of various kinds that go to support the same high level requirement. Currently our plan recognition algorithms are still at a conceptual stage, whereas a number of low level activity recognition algorithms and systems have been developed. Herein we present our model for plan recognition, providing a brief survey of the background literature. We also present some concrete results that we have achieved for activity recognition, emphasizing how these results are incorporated into the overall plan recognition system.

Information quality mapping in resource-constrained multi-modal data fusion system over wireless sensor network with losses

One of the approaches to reduce the complexity of application adaptation for a particular sensor network installation is to separate the application completely from the information acquisition level of the sensor network. However, in this case the question arises if the information obtained is good enough for the application. In this paper we describe the possible metrics of information quality (IQ) in the sensor network. We present a framework which addresses the problem of satisfying the IQ in the case of a dynamic system with resource constraints and communication losses. The framework is based on the dynamic Bayesian network model. The framework is built on a base of a constraint optimization problem which takes into account all the levels of information processing, from measurement to aggregation to data delivery by the network.

Data loss regulation to ensure information quality in sensor networks

In this paper, we consider a simple model for information loss as data propagates through a sensor network in response to continuous queries. We present an admission control scheme based on bounds placed on data loss probability, for sensors that contribute to aggregate query results. We expect that it will be possible to regulate this probability and thus guarantee the completeness of information obtained from the sensors. Our scheme uses the stochastic model of wireless data channel based on Pareto distribution of total waiting and service time. The parameters of distribution are obtained by measurement. Analyzed the loss of data units due to three reasons: overflow of network buffer; aggregation buffer and due to delay beyond timeout after which intermediate node assumes that the data was lost. Although the admission scheme adds some additional overhead to the sensor network operation, it is still limited to the cases when a new query is disseminated or a query mapping to resources is changing.

Multi-modal sensing smart spaces embedded with WSN based image camera

In this paper we discuss the use of low frame rate image cameras on a WSN in order to gather micro-context information in the context of smart homes and smart living spaces for the elderly. These simple devices are an attractive alternative to their more heavy duty counterparts since they can gather ambient image data at a rate that is amenable to the ambient space that they are in without much infrastructural support or modification. We propose their use in a multi-modal sensing environment where information from other ambient sensors may be mixed and matched in order to provide intelligence about the space and the activities of the subjects within the space. Their compelling use case, which includes their light weight and ease of mobility makes them a good candidate for a multi-modal sensing smart space. In this paper we introduce our work on architecture of the smart space and the implementation of the feature extraction using the image camera.