In recent years, there is a rapid increase in the population of elderly people. However, elderly people may suffer from the consequences of cognitive decline, which is a mental health disorder that primarily affects cognitive abilities such as learning, memory, etc. As a result, the elderly people may get dependent on caregivers to complete daily life tasks. Detecting the early indicators of dementia before it gets worsen and warning the caregivers and medical doctors would be helpful for further diagnosis. In this paper, the problem of activity recognition and abnormal behaviour detection is investigated for elderly people with dementia. First of all, the paper presents a methodology for generating synthetic data reflecting on some behavioural difficulties of people with dementia given the difficulty of obtaining real-world data. Secondly, the paper explores Convolutional Neural Networks (CNNs) to model patterns in activity sequences and detect abnormal behaviour related to dementia. Activity recognition is considered as a sequence labelling problem, while abnormal behaviour is flagged based on the deviation from normal patterns. Moreover, the performance of CNNs is compared against the state-of-art methods such as Naïve Bayes (NB), Hidden Markov Models (HMMs), Hidden Semi-Markov Models (HSMM), Conditional Random Fields (CRFs). The results obtained indicate that CNNs are competitive with those state-of-art methods.

近年来，老年人口迅速增加。但是，老年人可能会遭受认知能力下降的后果，认知能力下降是一种精神健康疾病，主要影响认知能力，例如学习，记忆等。结果，老年人可能会依赖看护者来完成日常生活任务。在痴呆症恶化之前检测其早期指标并警告护理人员和医生将有助于进一步诊断。本文研究了老年痴呆症患者的活动识别和异常行为检测问题。首先，本文提出了一种生成综合数据的方法，该方法反映了在获得现实世界数据困难的情况下痴呆症患者的某些行为困难。其次，本文探索了卷积神经网络（CNN），以对活动序列中的模式进行建模并检测与痴呆症相关的异常行为。活动识别被认为是序列标记问题，而基于与正常模式的偏差来标记异常行为。此外，将CNN的性能与最先进的方法（如朴素贝叶斯（NB），隐马尔可夫模型（HMM），隐半马尔可夫模型（HSMM），条件随机场（CRF）等进行了比较。获得的结果表明，CNN与那些最新方法具有竞争性。而根据与正常模式的偏差来标记异常行为。此外，将CNN的性能与最先进的方法（如朴素贝叶斯（NB），隐马尔可夫模型（HMM），隐半马尔可夫模型（HSMM），条件随机场（CRF）等进行了比较。获得的结果表明，CNN与那些最新方法具有竞争性。而根据与正常模式的偏差来标记异常行为。此外，将CNN的性能与最先进的方法进行比较，例如朴素贝叶斯（NB），隐马尔可夫模型（HMM），隐半马尔可夫模型（HSMM），条件随机场（CRF）。获得的结果表明，CNN可以与那些最新方法竞争。