Assessing pain in non-verbal patients is challenging, often depending on clinical judgment which can be unreliable due to fluctuations in vital signs caused by underlying medical conditions. To date, there is a notable absence of objective diagnostic tests to aid healthcare practitioners in pain assessment, especially affecting critically-ill or advanced dementia patients. Neurophysiological information, i.e., functional near-infrared spectroscopy (fNIRS) or electroencephalogram (EEG), unveils the brain’s active regions and patterns, revealing the neural mechanisms behind the experience and processing of pain. This study focuses on assessing pain via the analysis of fNIRS signals combined with machine learning, utilising multiple fNIRS measures including oxygenated haemoglobin ($\Delta HB{O}_2$) and deoxygenated haemoglobin ($\Delta HHB$). Initially, a channel selection process filters out highly contaminated channels with high-frequency and high-amplitude artifacts from the 24-channel fNIRS data. The remaining channels are then preprocessed by applying a low-pass filter and common average referencing to remove cardio-respiratory artifacts and common gain noise, respectively. Subsequently, the preprocessed channels are averaged to create a single time series vector for both $\Delta HB{O}_2$ and $\Delta HHB$ measures. From each measure, ten statistical features are extracted and fusion occurs at the feature level, resulting in a fused feature vector. The most relevant features, selected using the Minimum Redundancy Maximum Relevance method, are passed to a Support Vector Machines classifier. Using leave-one-subject-out cross validation, the system achieved an accuracy of $68.51\%\pm 9.02\%$ in a multi-class task (No Pain, Low Pain, and High Pain) using a fusion of $\Delta HB{O}_2$ and $\Delta HHB$. These two measures collectively demonstrated superior performance compared to when they were used independently. This study contributes to the pursuit of an objective pain assessment and proposes a potential biomarker for human pain using fNIRS.

Parkinson's disease (PD) causes impairments in cortical structures leading to motor and cognitive symptoms. While common disease management and treatment strategies mainly depend on the subjective assessment of clinical scales and patients' diaries, research in recent years has focused on advances in automatic and objective tools to help with diagnosing PD and determining its severity. Due to the link between brain structure deficits and physical symptoms in PD, objective brain activity and body motion assessment of patients have been studied in the literature. This study aimed to explore the relationship between brain activity and body motion measures of people with PD to look at the feasibility of diagnosis or assessment of PD using these measures. In this study, we summarised the findings of 24 selected papers from the complete literature review using the Scopus database. Selected studies used both brain activity recording using functional near-infrared spectroscopy (fNIRS) and motion assessment using sensors for people with PD in their experiments. Results include: 1) The most common study protocol is a combination of single tasks. 2) Prefrontal cortex is mostly studied region of interest in the literature. 3) Oxygenated haemoglobin (HbO $_{2}$ ) concentration is the predominant metric utilised in fNIRS, compared to deoxygenated haemoglobin (HHb). 4) Motion assessment in people with PD is mostly done with inertial measurement units (IMUs) and electronic walkway. 5) The relationship between brain activity and body motion measures is an important factor that has been neglected in the literature.

Parkinson’s disease (PD) leads to impairments in cortical structures, resulting in motor and cognitive symptoms. Given the connection between brain structure deficits and physical symptoms in PD, assessing objective brain activity and body motion could provide valuable insights for PD assessment and understanding its underlying mechanisms. This study aimed to explore the connection between brain activity and body movement metrics in a group of individuals with PD and an age-matched healthy control (HC) group. The goal was to evaluate the feasibility of using brain and body motion measures for assessing PD. Participants from both groups underwent the Timed Up and Go (TUG) test under three conditions: simple TUG, cognitive dual-task TUG (CDTUG), and motor dual-task TUG (MDTUG). Key findings include: Both groups exhibited similar activation patterns in the prefrontal cortex (PFC) during the simple TUG, with motor performance differences observed in cadence. During CDTUG, both groups showed the highest PFC activation with more pronounced motor impairments, such as higher stride and step time. During MDTUG, the HC group exhibited significantly higher PFC activity compared to the PD group. While both groups had similar patterns of activation in PFC area while TUG and CDTUG, they showed distinct behaviour during MDTUG. These results suggest that motor and cognitive impairments in PD are more pronounced during complex activities. While MDTUG effectively differentiated between PD and HC groups, the findings indicate that both cognitive and motor dual-tasks are essential for comprehensive PD assessment.

Parkinson’s disease is a progressive neurodegenerative disorder where diagnosis is essential for effective management. However, current diagnostic approaches often lack sensitivity during the early clinical stages of the disease. This study investigates the potential of wearable inertial measurement units combined with machine learning techniques for detecting Parkinson’s disease during mobility tasks. Data were collected from 28 individuals diagnosed with Parkinson’s disease and 34 age-matched healthy controls while performing the Timed Up and Go test and its dual-task variants. A set of gait and mobility features was extracted and refined using two feature selection techniques. Four machine learning models, including Support Vector Machine, Logistic Regression, Linear Discriminant Analysis, and Gradient Boosting, were trained to classify participants. Support Vector Machine and Logistic Regression achieved the highest classification accuracy of 95%. Key discriminative features included turn duration, sit-to-stand time, and stand-to-sit time, which demonstrated potential as objective digital biomarkers for early-stage Parkinson’s disease detection. Additionally, higher motor impairment scores were associated with increased shank range of motion and prolonged turn duration, particularly during dual-task conditions. These findings highlight the potential of wearable sensor-based mobility assessments combined with machine learning as a non-invasive, accessible, and reliable tool for early-stage detection and monitoring of Parkinson’s disease.

Parkinson’s disease (PD) is a neurodegenerative disorder where early diagnosis is crucial for effective management. However, current diagnostic methods are often invasive or delayed, hindering early intervention. This study evaluates the effectiveness of combining functional near-infrared spectroscopy (fNIRS) with machine learning to distinguish individuals with PD from age-matched controls. Data were collected using fNIRS from 28 people with PD and 32 age-matched controls while performing the Timed Up and Go (TUG) test under three conditions: simple TUG, cognitive dual-task TUG and motor dual-task TUG. Changes in cerebral blood oxygenation in the prefrontal cortex (PFC) were analysed using four machine learning models: Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Random Forest (RF) and Extreme Gradient Boosting (XGB), along with statistical analyses. Two feature selection models identified key features and channels for differentiating PD from controls. The SVM model achieved the highest accuracy (0.85 \(\pm\) 0.35) in distinguishing PD from CG. Feature selection and statistical analysis showed that dual-task activities were more effective than simple tasks for distinguishing PD from CG. Specific PFC subregions exhibited distinct activation patterns, which could serve as potential biomarkers for PD detection. Combining fNIRS with machine learning shows promise for PD diagnosis, with dual-task activities enhancing accuracy. Further investigation into PFC subregion behaviour could reveal stronger biomarkers.