# Joint Learning of Temporal Models to Handle Imbalanced Data for Human Activity Recognition
## 1 Introduction  
Human activity recognition (HAR) is the active research field for monitoring human behaviours, which stimulates various applications in fields healthcare monitoring [1], security monitoring [2], and resident situation assessment [3] and behaviour pattern recognition in pro-active home care [4]. In the home care scenario, HAR is a key component of smart home technology that makes independent living as a viable solution for elderly people, and thus enhances and maintains the quality of life and care [5, 6]. Smart home settings are often referred to as Ambient Assisted Living (AAL), and their main purpose is to remotely monitor and assess the wellness of older adults and people with dementia or other relevant disabilities. Overall, smart homes with human activities monitoring have been used for transparent surrounding context representation, which have enabled various health technology applications, such as disease progress and recovery tracking, or anomaly detection with a typical example of fall detection. Additionally, the recent advancement of machine learning has significantly progressed HAR systems and achieved performance improvements in many aspects of their applications, such as elderly-care alert systems and assistance in emergencies [7]. Long-term human activity monitoring yields feasibility to determine and assess the wellness. Specifically, activities, such as sleeping, eating, and showering in smart homes, are key events to enable the tracking and assess of the functional health status of elderly people [8]. Furthermore, data collectedfrom multimodal sensors in a smart home can provide sufficient information for the recognition of Activities of Daily Living (ADL), behaviour pattern to achieve detection, and the postural and ambulatory activity recognition [9, 10]. Although deep learning techniques have been applied for ADL recognition, it is still challenging and remains an open research issue to build an accurate HAR system due to the high diversity of human activities. Besides, the frequency variance of human activities is usually imbalanced leading to additional challenges. When building a machine learning model with an imbalanced dataset, it tends to partially or completely ignore the minority classes in order to achieve satisfied overall accuracy. For example, in HAR datasets, cooking, watching TV in the living room, and sleeping usually occur in a higher frequency than showering and snack eating. Different from the existing methods, this paper aims to improve HAR from imbalanced smart home datasets, especially for less represented classes. Although several past and recent studies have been conducted on the imbalanced class problem [11, 12], there is a lack of relevant empirical work for HAR. Classical machine learning methods in the form of individual learning or ensemble learning, such as support vector machine, decision tree, random forest, naive Bayes, hidden Markov models, and their ensembles, have been used to balance between maximising the accuracy of classification on minority classes and minimising the total recognition error [13, 14]. These classical methods have shown a certain level of recognition results, but these methods mainly rely on hand-crafted and classical heuristic feature extraction, which could be limited by the availability of knowledge domain experts [15]. A natural variation within each human physical activity is repeatedly presented in recorded datasets of a smart home environment and is often seen to fluctuate even more amongst inhabitants. Human activities are temporally and spatially different, and the deployed sensors in smart homes vary, so building accurate HAR systems can be challenging based on traditional machine learning where features are typically hand-crafted. Hence, discovering more systematic approaches to extract features from raw smart home datasets has drawn increasing research interests [16].