Duration models for activity recognition and prediction in buildings using Hidden Markov Models

Activity recognition and prediction in buildings can have multiple positive effects in buildings: improve elderly monitoring, detect intrusions, maximize energy savings and optimize occupant comfort. In this paper we apply human activity recognition by using data coming from a network of motion and door sensors distributed in a Smart Home environment. We use Hidden Markov Models (HMM) as the basis of a machine learning algorithm on data collected over an 8-month period from a single-occupant home available as part of the WSU CASAS Smart Home project. In the first implementation the HMM models 24 hours of activities and classifies them in 8 distinct activity categories with an accuracy rate of 84.6%. To improve the identification rate and to help detect potential abnormalities related with the duration of an activity (i.e. when certain activities last too much), we implement minimum duration modeling where the algorithm is forced to remain in a certain state for a specific amount of time. Two subsequent implementations of the minimum duration HMM (mean-based length modeling and quantile length modeling) yield a further 2% improvement of the identification rate. To predict the sequence of activities in the future, Artificial Neural Networks (ANN) are employed and identified activities clustered in 3 principal activity groups with an average accuracy rate of 71-77.5%, depending on the forecasting window. To explore the energy savings potential, we apply thermal dynamic simulations on buildings in central European climate for a period of 65 days during the winter and we obtain energy savings for space heating of up to 17% with 3-hour forecasting for two different types of buildings

Duration models for activity recognition and prediction in buildings using hidden Markov models

Activity recognition and prediction in buildings can have multiple positive effects in buildings: improve elderly monitoring, detect intrusions, maximize energy savings and optimize occupant comfort. In this paper we apply human activity recognition by using data coming from a network of motion and door sensors distributed in a Smart Home environment. We use Hidden Markov Models (HMM) as the basis of a machine learning algorithm on data collected over an 8-month period from a single-occupant home available as part of the WSU CASAS Smart Home project. In the first implementation the HMM models 24 hours of activities and classifies them in 8 distinct activity categories with an accuracy rate of 84.6%. To improve the identification rate and to help detect potential abnormalities related with the duration of an activity (i.e. when certain activities last too much), we implement minimum duration modeling where the algorithm is forced to remain in a certain state for a specific amount of time. Two subsequent implementations of the minimum duration HMM (mean-based length modeling and quantile length modeling) yield a further 2% improvement of the identification rate. To predict the sequence of activities in the future, Artificial Neural Networks (ANN) are employed and identified activities clustered in 3 principal activity groups with an average accuracy rate of 71-77.5%, depending on the forecasting window. To explore the energy savings potential, we apply thermal dynamic simulations on buildings in central European climate for a period of 65 days during the winter and we obtain energy savings for space heating of up to 17% with 3-hour forecasting for two different types of buildings

Intent sensing for assistive technology

This thesis aims to develop systems for intent sensing – the measurement and prediction of what it is that a user wants to happen. Being able to sense intent could be hugely beneficial for control of assistive devices, and could make a great impact on the wider medical device industry. Initially, a literature review is performed to determine the current state-of-the-art for intent sensing, and identifies that a holistic intent sensing system that properly captures all aspects of intent has not yet been developed. This is therefore followed by the development of such a novel intent sensing system. To achieve this, algorithms are developed to combine multiple sensors together into a modular Probabilistic Sensor Network. The performance of such a network is modelled mathematically, with these models tested and verified on real data. The intent sensing system then developed from these models is tested for sensing modalities such as Electromyography (EMG), motion data from Inertial Measurement Units (IMUs), and audio. The benefits of constructing a modular system in this way are demonstrated, showcasing improvement in accuracy with a fixed amount of training data, and in robustness to sensor unavailability – a common problem in prosthetics, where sensor lift-off from the skin is a frequent issue. Initially, the algorithm is developed to classify intent after activity completion, and this is then developed to allow it to run in real-time. Different classification methods are proposed and tested including K-nearest-neighbours (KNN), before deep learning is selected as an effective classifier for this task. In order to apply deep learning without requiring a prohibitively large training data set, a time-segmentation method is developed to limit the complexity of the model and make better use of the available data. Finally, the techniques developed in the thesis are combined into a single continuous, multi-modal intent sensing system that is modular in both sensor composition and in time. At every stage of this process, the algorithms are tested against real data, initially from non-disabled volunteer participants and in the later chapters on data from patients with Parkinson’s disease (a group who may benefit greatly from an intent sensing system). The final system is found to achieve an accuracy of 97.4% almost immediately after activity inception, increasing to 99.9918% over the course of the activity. This high accuracy can be seen both in the patient group and the control group, demonstrating that intent sensing is indeed viable with currently available technology, and should be further developed into future control systems for assistive devices to improve quality of life for both disabled and non-disabled users alike