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.

Functional near-infrared spectroscopy (fNIRS) can be used to study activity patterns in different brain cortical areas involved in balance control. This systematic review aims to report on studies in which balance performance has been quantitatively assessed concurrent with fNIRS neuroimaging. Following the PRISMA guidelines, relevant keywords were used for the search through the Scopus and Web of Science databases. Sixty-eight studies met the inclusion criteria and were imported for data extraction. Information on balance assessment protocols, alterations to the balance control loop, brain regions of interest, fNIRS parameters, the relationships found between brain activity and balance performance, and participant cohort types was extracted. The common balance tasks in fNIRS studies were standing and walking. Standing balance control was mainly measured through sway parameters using force platforms. Walking performance was evaluated through gait parameters mostly assessed by floor sensors or inertial sensors. Some of the balance tasks were challenged through sensory manipulation or dual task interference. Brain activity monitoring via fNIRS was mainly utilized to measure oxygenated haemoglobin concentration in frontal cortex. Out of the 68 included articles, 22 studies investigated and found the relationships between activity patterns in different cortical areas and balance measures. In 32 studies, the effects of different factors such as long-term, biological, and psychological conditions on brain activity and balance performance were studied. This study provides a systematic review on fNIRS studies in which quantitative balance assessment is employed to provide a better understanding of neuromotor control of balance.

Interlimb coordination variability analysis can shed light into the dynamics of higher order coordination and motor control. However, it is not clear how the interlimb coordination of people with no known injuries change in similar activities with increasing difficulty. This study aimed to ascertain if the interlimb coordination variability range and patterns of healthy participants change in different unilateral functional tasks with increasing complexity and whether leg dominance affects the interlimb coordination variability. In this cross-sectional study fourteen younger participants with no known injuries completed three repeated unilateral sit-to-stands (UniSTS), step-ups (SUs), and continuous-hops (Hops). Using four inertial sensors mounted on the lower legs and thighs, angular rotation of thighs and shanks were recorded. Using Hilbert transform, the phase angle of each segment and then the continuous relative phase (CRP) of the two segments were measured. The CRP is indicative of the interlimb coordination. Finally, the linear and the nonlinear shank-thigh coordination variability of each participant in each task was calculated. The results show that the linear shank-thigh coordination variability was significantly smaller in the SUs compared to both UniSTS and Hops in both legs. There were no significant differences found between the latter two tests in their linear coordination variability. However, Hops were found to have significantly larger nonlinear shank-thigh coordination variability compared to the SUs and the UniSTS. This can be due to larger vertical and horizontal forces required for the task and can reveal inadequate motor control during the movement. The combination of nonlinear and linear interlimb coordination variability can provide more insight into human movement as they measure different aspects of coordination variability. It was also seen that leg dominance does not affect the lower limb coordination variability in participants with no known injuries. The results should be tested in participants recovering from lower limb injuries.