Ching-Hu Lu

Robust Location-Aware Activity Recognition Using Wireless Sensor Network in an Attentive Home

This paper presents a robust location-aware activity recognition approach for establishing ambient intelligence applications in a smart home. With observations from a variety of multimodal and unobtrusive wireless sensors seamlessly integrated into ambient-intelligence compliant objects (AICOs), the approach infers a single residents interleaved activities by utilizing a generalized and enhanced Bayesian Network fusion engine with inputs from a set of the most informative features. These features are collected by ranking their usefulness in estimating activities of interest. Additionally, each feature reckons its corresponding reliability to control its contribution in cases of possible device failure, therefore making the system more tolerant to inevitable device failure or interference commonly encountered in a wireless sensor network, and thus improving overall robustness. This work is part of an interdisciplinary Attentive Home pilot project with the goal of fulfilling real human needs by utilizing context-aware attentive services. We have also created a novel application called ldquoActivity Maprdquo to graphically display ambient-intelligence-related contextual information gathered from both humans and the environment in a more convenient and user-accessible way. All experiments were conducted in an instrumented living lab and their results demonstrate the effectiveness of the system.

A Reciprocal and Extensible Architecture for Multiple-Target Tracking in a Smart Home

Every home has its own unique considerations for location-aware applications. This makes a flexible architecture very crucial for efficiently integrating various tracking devices/models for adapting to real human needs. Here, we propose a reciprocal and extensible architecture to flexibly add/remove tracking sensors/models for tracking multiple targets in a smart home. Regarding tracking devices, we employ sensors from two different categories, those with seamless sensors and those with seamful ones. This allows us to take human-centric needs into consideration and to facilitate reciprocal and cooperative interaction among sensors from the two categories. Such reciprocal cooperation aims to increase the accuracy of location estimates and to compensate for the limitations of each sensor or a tracking algorithm, which allows us to track multiple targets simultaneously in a more reliable way. Moreover, the approach demonstrated in this paper can serve as a guideline to help users customize sensor arrangements to fulfill their requirements. Our experimental results, which comprise three tracking scenarios using a load sensory floor as the seamless sensor and RF identifications (RFIDs) as seamful sensors, demonstrate the effectiveness of the proposed architecture.

Strategies for inference mechanism of conditional random fields for multiple-resident activity recognition in a smart home

Multiple-resident activity recognition is a major challenge for building a smart-home system. In this paper, conditional random fields (CRFs) are chosen as our activity recognition models for overcoming this challenge. We evaluate our proposed approach with several strategies, including conditional random field with iterative inference and the one with decomposition inference, to enhance the commonly used CRFs so that they can be applied to a multipleresident environment. We use the multi-resident CASAS data collected at WSU (Washington State University) to validate these strategies. The results show that data association of non-obstructive sensor data is of vital importance to improve the performance of activity recognition in a multiple-resident environment. Furthermore, the study also suggests that human interaction be taken into consideration for further accuracy improvement.

Interaction models for multiple-resident activity recognition in a smart home

Multi-resident activity recognition is among a key enabler in many context-aware applications in a smart home. However, most of prior researches ignore the potential interactions among residents in order to simplify problem complexity. On the other hand, multiple-resident activities are usually recognized using cameras or wearable sensors. However, due to human-centric concerns, it is more preferable to avoid using obtrusive sensors. In this paper, we propose dynamic Bayesian networks which extend coupled hidden Markov models (CHMMs) by adding some vertices to model both individual and cooperative activities. In order to improve performance of the model, we categorize sensor observations based on data association and some domain knowledge to model multiple-resident activity patterns. We then validate the performance using a multi-resident dataset from WSU (Washington State University), which only includes non-obtrusive sensors. The experimental result shows that our model performs better than other baseline classifiers.

Context-aware home energy saving based on Energy-Prone Context

Energy overuse has caused many environmental and economic issues, so energy saving for household is challenging and important for a smart home. For home energy saving based on context-awareness, human activity is critical information since knowing what activities are undertaken is important for judging if energy consumed by appliances is well spent by users. Such contextual information is an important clue for providing an energy saving service. However, most of the prior works on home energy saving often ignore those appliances which are operating indirectly or implicitly related to the context. These factors may compromise the practicality and acceptability of most of the currently available energy saving systems, thus failing to meet real user needs. Therefore, we propose utilizing an Energy-Prone Context to model a context and its associated energy consumption. In addition, we also propose a systematic method to determine energy-saving services based on the Energy-Prone Contexts. Our experimental results demonstrate the effectiveness of the proposed approach.

Preference model assisted activity recognition learning in a smart home environment

Reliable recognition of activities from cluttered sensory data is challenging and important for a smart home to enable various activity-aware applications. In addition, understanding a users preferences and then providing corresponding services is substantial in a smart home environment. Traditionally, activity recognition and preference learning were dealt with separately. In this work, we aim to develop a hybrid system which is the first trial to model the relationship between an activity model and a preference model so that the resultant hybrid model enables a preference model to assist in recovering performance of activity recognition in a dynamic environment. More specifically, on-going activity which a user performs in this work is regarded as high level contexts to assist in building a users preference model. Based on the learned preference model, the smart home system provides more appropriate services to a user so that the hybrid system can better interact with the user and, more importantly, gain his/her feedback. The feedback is used to detect if there is any change in human behavior or sensor deployment such that the system can adjust the preference model and the activity model in response to the change. Finally, the experimental results confirm the effectiveness of the proposed approach.

Hybrid User-Assisted Incremental Model Adaptation for Activity Recognition in a Dynamic Smart-Home Environment

Identifying on-going activities for the provision of services that are capable of matching the needs of users poses a number of daunting challenges. Most existing approaches to activity recognition require training offline activity models before being applied to the identification of activities in real time. However, the dynamic nature of actual living environments can make previously learned activity models irrelevant. This study addressed the problem of learning and recognizing daily activities in a dynamic smart-home environment, using a novel approach referred to as hybrid user-assisted incremental model adaptation. This approach involves reconfiguring previously learned activity models within a dynamic environment, while pursuing maximum efficiency by using assistance from users as well as the system to annotate new training data. Experiments that are conducted in a fully equipped smart-home lab demonstrate the efficacy of the proposed approach.

Energy-Responsive Aggregate Context for Energy Saving in a Multi-Resident Environment

Human activity is among the critical information for a context-aware energy saving system since knowing what activities are undertaken is important for judging if energy is well spent. Most of the prior works on energy saving do not make the best of context-awareness especially in a multiuser environment to assist the energy saving system. In addition, they often ignore whether appliances are operating implicitly or explicitly related to the context. These factors may compromise the practicality and acceptability of most of the currently available energy saving systems, thus failing to meet real user needs. Therefore, we propose Energy-Responsive Aggregate Context (ERAC) to model multi-resident activities and their associated energy consumption. Based on the relationship, implicit or explicit, between a given appliance and its associated context, an energy saving system and its users can better determine whether the power consumed by the appliance is wasted. Our experimental results demonstrate the effectiveness of the proposed approach.

Active-learning assisted self-reconfigurable activity recognition in a dynamic environment

It is desirable to know a residents on-going activities before a robot or a smart system can provide attentive services to meet real human needs. This work addresses the problem of learning and recognizing human daily activities in a dynamic environment. Most currently available approaches learn offline activity models and recognize activities of interest on a real time basis. However, the activity models become outdated when human behaviors or device deployment have changed. It is a tedious and error-prone job to recollect data for retraining the activity models. In such a case, it is important to adapt the learnt activity models to the changes without much human supervision. In this work, we present a self-reconfigurable approach for activity recognition which reconfigures previously learnt activity models and infers multiple activities under a dynamic environment meanwhile pursuing minimal human efforts in relabeling training data by utilizing active-learning assistance.

Hide and Not Easy to Seek: A Hybrid Weaving Strategy for Context-Aware Service Provision in a Smart Home

Weaving computing technologies into a living environment without interfering with natural interactions is nontrivial. In this paper, we have proposed utilizing ambient-intelligence compliant object (AICO) to facilitate context-aware service provision in a smart home; furthermore, a hybrid weaving strategy, called Hide and Not Easy to Seek, is proposed for designing the weaving layer of each AICO and for popularizing ubiquitous computing. Seven weaving guidelines which combine seamless and seamful designs are learned from our actual instrumentation of a living lab and continuous cooperation with specialists from various domains. By following the proposed guidelines, our expectation is that people will be able to use the weaved technologies more naturally to accomplish everyday tasks.