Machine Learning Challenges For Automated Prompting In Smart Homes
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As the world's population ages, there is an increased prevalence of diseases related to aging, such as dementia. Caring for individuals with dementia is frequently associated with extreme physical and emotional stress, which often leads to depression. Smart home technology and advances in machine learning techniques can provide innovative solutions to reduce caregiver burden. One key service that caregivers provide is prompting individuals with memory limitations to initiate and complete daily activities. We hypothesize that sensor technologies combined with machine learning techniques can automate the process of providing reminder-based interventions or prompts. This dissertation focuses on addressing machine learning challenges that arise while devising an effective automated prompting system. Our first goal is to emulate natural interventions provided by a caregiver to individuals with memory impairments, by using a supervised machine learning approach to classify pre-segmented activity steps into prompt or no-prompt classes. However, the lack of training examples representing prompt situations causes imbalanced class distribution. We proposed two probabilistic oversampling techniques, RACOG and wRACOG, that help in better learning of the "prompt" class. Moreover, there are certain prompt situations where the sensor triggering signature is quite similar to the situations when the participant would probably need no prompt. The absence of sufficient data attributes to differentiate between prompt and no-prompt classes causes class overlap. We propose ClusBUS, a clustering-based under-sampling technique that identifies ambiguous data regions. ClusBUS preprocesses the data in order to give more importance to the minority class during classification.Our second goal is to automatically detect activity errors in real time, while an individual performs an activity. We propose a collection of one-class classification-based algorithms, known as DERT, that learns only from the normal activity patterns and without using any training examples for the activity errors. When evaluated on unseen activity data, DERT is able to identify abnormalities or errors, which can be potential prompt situations. We validate the effectiveness of the proposed algorithms in predicting potential prompt situations on the sensor data of ten activities of daily living, collected from 580 participants, who were part of two smart home studies.