Parkinson's disease (PD) is a complex neurodegenerative disease in the elderly. This disease has no cure, but assessing these motor symptoms will help slow down that progression. Inertial sensing-based wearable devices (ISWDs) such as mobile phones and smartwatches have been widely employed to analyse the condition of PD patients. However, most studies purely focused on a single activity or symptom, which may ignore the correlation between activities and complementary characteristics. In this paper, a novel technical pipeline is proposed for fine-grained classification of PD severity grades, which identify the most representative activities. We also propose a multi-activities combination scheme based on MDS-UPDRS. Utilizing this scheme, symptom-related and complementary activities are captured. We collected 85 PD subjects of different severity grades using a single wrist sensor. Our best results demonstrate F1 scores of 95.75% for PD diagnosis and the fine-grained classification accuracy of PD disease grade is 82.41% when combing 4 activities which improved by 11.02% over a single activity. The experiments and theoretical analyses can serve as a useful foundation for future investigations into the effect of proposed solutions for PD diagnosis in uncontrolled environment setup, ultimately leading to self-PD assessment in the home environment.

Alzheimer's disease (AD), the most prevalent dementia, gradually reduces the cognitive abilities of patients while also posing a significant financial burden on the healthcare system. A variety of multi-task learning methods have recently been proposed in order to identify potential MRI-related biomarkers and accurately predict the progression of AD. These methods, however, all use a predefined task relation structure that is rigid and insufficient to adequately capture the intricate temporal relations among tasks. Instead, we propose a novel mechanism for directly and automatically learning the temporal relation and constructing it as an Automatic Temporal relation Graph (AutoTG). We use the sparse group Lasso to select a universal MRI feature set for all tasks and particular sets for various tasks in order to find biomarkers that are useful for predicting the progression of AD. To solve the biconvex and non-smooth objective function, we adopt the alternating optimization and show that the two related sub-optimization problems are amenable to closed-form solution of the proximal operator. To solve the two problems efficiently, the accelerated proximal gradient method is used, which has the fastest convergence rate of any first-order method. We have preprocessed three latest AD datasets, and the experimental results verify our proposed novel multi-task approach outperforms several baseline methods. To demonstrate the high interpretability of our approach, we visualise the automatically learned temporal relation graph and investigate the temporal patterns of the important MRI features.

Objective: Recent quantification research on Parkinson's disease (PD) integrates wearable technology with machine learning methods, indicating a strong potential for practical applications. However, the effectiveness of these techniques is influenced by environmental settings and is hardly applied in real-world situations. This paper aims to propose an effective feature assessment framework to automatically rate the severity of PD motor symptoms from short-term motor tasks, and then classify different PD severity levels in the real world. Methods: This paper identified specific PD motor symptoms using a novel feature-assessment framework at both segment-level and sample-level. Features were selected after calculating SHapley Additive exPlanation(SHAP) value, and verified by different machine learning methods with appropriate parameters. This framework has been verified on real-world data from 100 PD patients performing Unified Parkinson's Disease Rating Scale(UPDRS)-recommended short motor tasks, each task lasting 20-50 seconds. Results: The sensitivity for recognizing motor fluctuations reached 88% in tremor recognition. Additionally, LightGBM achieved the highest accuracy for early detection(92.59%) and achieved 71.58% in fine-grained severity classification using 31 selected features. Conclusion: This paper reports the first effort to assess multi-level and multi-scale features for automatic quantification of motor symptoms and PD severity levels. The proposed framework has been proven effective in assessing key PD information for recognition during short-term tasks. Significance: The explanatory analysis of digital features in this study provides more prior knowledge for PD self-assessment in a free-living environment.

Parkinson’s disease (PD) is the second most prevalent dementia in the world. Wearable technology has been useful in the computer-aided diagnosis and long-term monitoring of PD in recent years. The fundamental issue remains how to assess the severity of PD using wearable devices in an efficient and accurate manner. However, in the real-world free-living environment, there are two difficult issues, poor annotation and class imbalance, both of which could potentially impede the automatic assessment of PD. To address these challenges, we propose a novel framework for assessing the severity of PD patient’s in a free-living environment. Specifically, we use clustering methods to learn latent categories from the same activities, while latent Dirichlet allocation (LDA) topic models are utilized to capture latent features from multiple activities. Then, to mitigate the impact of data imbalance, we augment bag-level data while retaining key instance prototypes. To comprehensively demonstrate the efficacy of our proposed framework, we collected a dataset containing wearable-sensor signals from 83 individuals in real-life free-living conditions. The experimental results show that our framework achieves an astounding 73.48% accuracy in the fine-grained (normal, mild, moderate, severe) classification of PD severity based on hand movements. Overall, this study contributes to more accurate PD self-diagnosis in the wild, allowing doctors to provide remote drug intervention guidance.

As an emerging learning paradigm, lifelong learning solves multiple consecutive tasks based upon previously accumulated knowledge. When facing with a new task, existing lifelong learning approaches need both input and desired output data to construct task models before knowledge transfer can succeed. However, labeling each task requires extensive labors and time, which can be prohibitive for real-world lifelong regression problems. To reduce this burden, we propose to incorporate unsupervised feature into lifelong regression via coupled dictionary learning, enabling to learn new tasks without target output data. Specifically, the input data for each task is encoded as unsupervised feature while both input and output data are used to construct task predictor. The unsupervised feature is linked with task predictor through two dictionaries that are coupled by a joint sparse representation. Because of the learned coupling between the two spaces, the task predictor for the new coming task can be recovered given only the input data. We further incorporate active task selection into this framework, enabling actively choosing tasks to learn in a task-efficient manner. Three case studies are used to evaluate the effectiveness of our method, in comparison with existing lifelong learning approaches. Results show that our method is able to accurately predict new tasks through unsupervised transfer, eliminating the need to label tasks before constructing the predictor.