Environmental impact of hybrid (reusable/single-use) ports versus single-use equivalents in robotic surgery.

Given the rise in robotic surgery, and parallel movement towards net zero carbon, sustainable healthcare systems, it is important that the environmental impact of robotic approaches is minimised. The majority of greenhouse gas emissions associated with robotic surgery have previously been associated with single-use items. Whilst switching from single-use products to hybrid equivalents (predominantly reusable, with a small single-use component) has previously been found to reduce the environmental impact of a range of products used for laparoscopic surgery, the generalisability of this to robotic surgery has not previously been demonstrated. In this life cycle assessment, use of hybrid 5 mm ports compatible with emerging robotic systems (143 g CO2e) was found to reduce the carbon footprint by 83% compared with using single-use equivalents (816 g CO2e), accompanied by reductions in fifteen out of eighteen midpoint environmental impact categories. For endpoint categories, there was an 81% reduction in impact on human health and species loss, and 82% reductions in resource depletion associated with using hybrid robotic 5 mm ports. Whilst the carbon footprint of 5 mm hybrid ports compatible with emerging robotic equipment was 70% higher than previous estimates of ports appropriate for conventional laparoscopic approaches, the six-fold reductions seen with hybrids in this analysis point to the generalisability of the finding that reusable or hybrid products have a lower carbon footprint when compared with single-use equivalents. Surgeons, procurement teams, and policy makers should encourage innovation towards clinically safe and effective robotic instruments with maximal reusable components.

Impaired proprioception and magnified scaling of proprioceptive error responses in chronic stroke.

BACKGROUND: Previous work has shown that ~ 50-60% of individuals have impaired proprioception after stroke. Typically, these studies have identified proprioceptive impairments using a narrow range of reference movements. While this has been important for identifying the prevalence of proprioceptive impairments, it is unknown whether these error responses are consistent for a broad range of reference movements. The objective of this study was to characterize proprioceptive accuracy as function of movement speed and distance in stroke. METHODS: Stroke (N = 25) and controls (N = 21) completed a robotic proprioception test that varied movement speed and distance. Participants mirror-matched various reference movement speeds (0.1-0.4 m/s) and distances (7.5-17.5 cm). Spatial and temporal parameters known to quantify proprioception were used to determine group differences in proprioceptive accuracy, and whether patterns of proprioceptive error were consistent across testing conditions within and across groups. RESULTS: Overall, we found that stroke participants had impaired proprioception compared to controls. Proprioceptive errors related to tested reference movement scaled similarly to controls, but some errors showed amplified scaling (e.g., significantly overshooting or undershooting reference speed). Further, interaction effects were present for speed and distance reference combinations at the extremes of the testing distribution. CONCLUSIONS: We found that stroke participants have impaired proprioception and that some proprioceptive errors were dependent on characteristics of the movement (e.g., speed) and that reference movements at the extremes of the testing distribution resulted in significantly larger proprioceptive errors for the stroke group. Understanding how sensory information is utilized across a broad spectrum of movements after stroke may aid design of rehabilitation programs.

The effects of dance interventions on physical function and quality of life among middle-aged and older adults: A systematic review.

BACKGROUND: Fundamental physical functions such as postural control and balance are vital in preserving everyday life, affecting an individual's quality of life. Dance is a physical activity that offers health advantages across various life stages. Nevertheless, the effects of dance interventions on physical function, postural control, and quality of life among older adults have remained underexplored. The review aimed to examine the strength of evidence for dance interventions on physical function and quality of life among middle-aged and older adults. METHODS: A systematic review was conducted across four databases (PubMed, Cochrane Library, Web of Science, and Medline), focusing on studies involving more than four weeks of dance interventions. MeSH terms [dance or dance intervention or dance rehabilitation or dance movement] and [motor function or functional capacity or postural control or functional mobility or mobility or postural balance or balance or flexibility or gait] and [well-being or quality of life or life satisfaction] were utilized in the search. This review was registered in the PROSPERO database (CRD42023422857). Included studies were assessed using the Cochrane Risk of Bias. RESULTS: The search revealed 885 studies, and 16 met the inclusion criteria. The effects of various dance genres on physical functions and quality of life were compared. Most studies showed that dance intervention improved physical function, balance, postural control and quality of life. Dance intervention showed a high level of adherence compared to physiotherapy, self-care, conventional therapy, and aerobic and resistance exercise. CONCLUSION: In terms of improving physical function and quality of life, structured dance is a safe and relatively effective alternative to exercise. Note the effect of movement selection and intensity in the dance interventions. Dance with music may increase participants' interest, encouraging more physical activity among middle-aged and older adults.

Exploring data sources and mathematical approaches for estimating human mobility rates and implications for understanding COVID-19 dynamics: a systematic literature review.

Human mobility, which refers to the movement of people from one location to another, is believed to be one of the key factors shaping the dynamics of the COVID-19 pandemic. There are multiple reasons that can change human mobility patterns, such as fear of an infection, control measures restricting movement, economic opportunities, political instability, etc. Human mobility rates are complex to estimate as they can occur on various time scales, depending on the context and factors driving the movement. For example, short-term movements are influenced by the daily work schedule, whereas long-term trends can be due to seasonal employment opportunities. The goal of the study is to perform literature review to: (i) identify relevant data sources that can be used to estimate human mobility rates at different time scales, (ii) understand the utilization of variety of data to measure human movement trends under different contexts of mobility changes, and (iii) unraveling the associations between human mobility rates and social determinants of health affecting COVID-19 disease dynamics. The systematic review of literature was carried out to collect relevant articles on human mobility. Our study highlights the use of three major sources of mobility data: public transit, mobile phones, and social surveys. The results also provides analysis of the data to estimate mobility metrics from the diverse data sources. All major factors which directly and indirectly influenced human mobility during the COVID-19 spread are explored. Our study recommends that (a) a significant balance between primitive and new estimated mobility parameters need to be maintained, (b) the accuracy and applicability of mobility data sources should be improved, (c) encouraging broader interdisciplinary collaboration in movement-based research is crucial for advancing the study of COVID-19 dynamics among scholars from various disciplines.

Is more always better? Effectiveness of constraint-induced movement therapy in children with high-risk or unilateral cerebral palsy (0-6 years): Systematic review and meta-analysis.

BACKGROUND: While constraint-induced movement therapy is strongly recommended as an intervention for infants with unilateral cerebral palsy, the optimal dosage remains undefined. This systematic review aims to identify the most effective level of intensity of constraint-induced movement therapy to enhance manual function in infants at high risk of asymmetric brain lesions or unilateral cerebral palsy diagnosis. METHODS: This systematic review with meta-analysis encompassed a comprehensive search across four electronic databases to identify articles that met the following criteria: randomised controlled trials, children aged 0-6 with at high risk or with unilateral cerebral palsy, and treatment involving constraint-induced movement therapy for upper limb function. Studies with similar outcomes were pooled by calculating the standardised mean difference score for each subgroup, and subgroups were stratified every 30 h of total intervention dosage (30-60, 61-90, >90 h). Risk of bias was assessed with Cochrane Collaboration's tool. RESULTS: Seventeen studies were included. Meta-analyses revealed significant differences among subgroups. The 30-60 h subgroup showed a weak effect for spontaneous use of the affected upper limb during bimanual performance, grasp function, and parents' perception of how often children use their affected upper limb. Additionally, this subgroup demonstrated a moderate effect for the parents' perception of how effectively children use their affected upper limb. CONCLUSIONS: Using a dosage ranging from 30 to 60 h when applying a constraint-induced movement therapy protocol holds promise as the most age-appropriate and cost-effectiveness approach for improving upper limb functional outcomes and parent's perception.

Intra and inter-rater remote assessment of bradykinesia in Parkinson's disease.

INTRODUCTION: Reliable assessment of individuals with Parkinson's disease (PD) is essential for providing adequate treatment. Clinical assessment is a complex and time-consuming task, especially for bradykinesia, since its evaluation can be influenced by the degree of experience of the examiner, patient collaboration and individual bias. Improvement of the clinical evaluation can be obtained by considering assessments from several professionals. However, this is only true when inter and intra-rater agreement are high. Recently, the Movement Disorder Society highlighted, during the COVID-19 pandemic, the need to develop and validate technologies for remote assessment of the motor status of people with PD. Thus, this study introduces an objective strategy for the remote evaluation of bradykinesia using multi-specialist analysis. METHODS: Twelve volunteers with PD participated and these were asked to execute finger tapping, hand opening/closing and pronation/supination movements. Each task was recorded and rated by fourteen PD health experts for each patient. The scores were assessed on an individual basis. Intra and inter-rater agreement and correlation were estimated. RESULTS: The results showed that agreements and correlations between experienced examiners were high with low variability. In addition, group analysis was noted as possessing the potential to solve individual inconsistency bias. CONCLUSION: Furthermore, this study demonstrated the need for a group with prior training and experience, along with indicating the importance for the development of a clinical protocol that can use telemedicine for the evaluation of individuals with PD, as well as the inclusion of a specialized mediating group. In Addition, this research helps to the development of a valid remote assessment of bradykinesia.

Perceptual formant discrimination during speech movement planning.

Evoked potential studies have shown that speech planning modulates auditory cortical responses. The phenomenon's functional relevance is unknown. We tested whether, during this time window of cortical auditory modulation, there is an effect on speakers' perceptual sensitivity for vowel formant discrimination. Participants made same/different judgments for pairs of stimuli consisting of a pre-recorded, self-produced vowel and a formant-shifted version of the same production. Stimuli were presented prior to a "go" signal for speaking, prior to passive listening, and during silent reading. The formant discrimination stimulus /uh/ was tested with a congruent productions list (words with /uh/) and an incongruent productions list (words without /uh/). Logistic curves were fitted to participants' responses, and the just-noticeable difference (JND) served as a measure of discrimination sensitivity. We found a statistically significant effect of condition (worst discrimination before speaking) without congruency effect. Post-hoc pairwise comparisons revealed that JND was significantly greater before speaking than during silent reading. Thus, formant discrimination sensitivity was reduced during speech planning regardless of the congruence between discrimination stimulus and predicted acoustic consequences of the planned speech movements. This finding may inform ongoing efforts to determine the functional relevance of the previously reported modulation of auditory processing during speech planning.

A Three-branch Jointed Feature and Topology Decoder guided by game-theoretic interactions for temporomandibular joint segmentation.

Segmentation of the temporomandibular joint (TMJ) disc and condyle from magnetic resonance imaging (MRI) is a crucial task in TMJ internal derangement research. The automatic segmentation of the disc structure presents challenges due to its intricate and variable shapes, low contrast, and unclear boundaries. Existing TMJ segmentation methods often overlook spatial and channel information in features and neglect overall topological considerations, with few studies exploring the interaction between segmentation and topology preservation. To address these challenges, we propose a Three-Branch Jointed Feature and Topology Decoder (TFTD) for the segmentation of TMJ disc and condyle in MRI. This structure effectively preserves the topological information of the disc structure and enhances features. We introduce a cross-dimensional spatial and channel attention mechanism (SCIA) to enhance features. This mechanism captures spatial, channel, and cross-dimensional information of the decoded features, leading to improved segmentation performance. Moreover, we explore the interaction between topology preservation and segmentation from the perspective of game theory. Based on this interaction, we design the Joint Loss Function (JLF) to fully leverage the features of segmentation, topology preservation, and joint interaction branches. Results on the TMJ MRI dataset demonstrate the superior performance of our TFTD compared to existing methods.

LightPRA: A Lightweight Temporal Convolutional Network for Automatic Physical Rehabilitation Exercise Assessment.

Research evidence shows that physical rehabilitation exercises prescribed by medical experts can assist in restoring physical function, improving life quality, and promoting independence for physically disabled individuals. In response to the absence of immediate expert feedback on performed actions, developing a Human Action Evaluation (HAE) system emerges as a valuable automated solution, addressing the need for accurate assessment of exercises and guidance during physical rehabilitation. Previous HAE systems developed for the rehabilitation exercises have focused on developing models that utilize skeleton data as input to compute a quality score for each action performed by the patient. However, existing studies have focused on improving scoring performance while often overlooking computational efficiency. In this research, we propose LightPRA (Light Physical Rehabilitation Assessment) system, an innovative architectural solution based on a Temporal Convolutional Network (TCN), which harnesses the capabilities of dilated causal Convolutional Neural Networks (CNNs). This approach efficiently captures complex temporal features and characteristics of the skeleton data with lower computational complexity, making it suitable for real-time feedback provided on resource-constrained devices such as Internet of Things (IoT) devices and Edge computing frameworks. Through empirical analysis performed on the University of Idaho-Physical Rehabilitation Movement Data (UI-PRMD) and KInematic assessment of MOvement for remote monitoring of physical REhabilitation (KIMORE) datasets, our proposed LightPRA model demonstrates superior performance over several state-of-the-art approaches such as Spatial-Temporal Graph Convolutional Network (STGCN) and Long Short-Term Memory (LSTM)-based models in scoring human activity performance, while exhibiting lower computational cost and complexity.

Regional shear wave speeds track regional axial stress in nonuniformly loaded fibrous soft tissues.

Ligaments and tendons undergo nonuniform deformation during movement. While deformations can be imaged, it remains challenging to use such information to infer regional tissue loading. Shear wave tensiometry is a promising noninvasive technique to gauge axial stress and is premised on a tensioned beam model. However, it is unknown whether tensiometry can predict regional stress in a nonuniformly loaded structure. The objectives of this study were to (1) determine whether regional shear wave speed tracks regional axial stress in nonuniformly loaded fibrous soft tissues, and (2) determine the sensitivity of regional axial stress and shear wave speed to nonuniform load distribution and fiber alignment. We created a representative set of 12,000 dynamic finite element models of a fibrous soft tissue with probabilistic variations in fiber alignment, stiffness, and aspect ratio. In each model, we applied a randomly selected nonuniform load distribution, and then excited a shear wave and tracked its regional propagation. We found that regional shear wave speed was an excellent predictor of the regional axial stress (RMSE = 0.57 MPa) and that the nature of the regional shear wave speed-stress relationship was consistent with a tensioned beam model (R2 = 0.99). Variations in nonuniform load distribution and fiber alignment did not substantially alter the wave speed-stress relationship, particularly at higher loads. Thus, these findings suggests that shear wave tensiometry could provide a quantitative estimate of regional tissue stress in ligaments and tendons.

Spontaneous eye movements reflect the representational geometries of conceptual spaces.

Functional neuroimaging studies indicate that the human brain can represent concepts and their relational structure in memory using coding schemes typical of spatial navigation. However, whether we can read out the internal representational geometries of conceptual spaces solely from human behavior remains unclear. Here, we report that the relational structure between concepts in memory might be reflected in spontaneous eye movements during verbal fluency tasks: When we asked participants to randomly generate numbers, their eye movements correlated with distances along the left-to-right one-dimensional geometry of the number space (mental number line), while they scaled with distance along the ring-like two-dimensional geometry of the color space (color wheel) when they randomly generated color names. Moreover, when participants randomly produced animal names, eye movements correlated with low-dimensional similarity in word frequencies. These results suggest that the representational geometries used to internally organize conceptual spaces might be read out from gaze behavior.

Movement as a Gateway to Participation for Individuals With Neuromotor Conditions: A Scoping Review.

PURPOSE: The purpose of this scoping review was to examine and analyze the developmental and rehabilitation literature related to movement and participation at key points of transition for individuals with neuromotor conditions. METHODS: Arksey and O'Malley's scoping review protocol was applied, and 37 articles were included. Extracted data included population, developmental transition points, movement opportunity, type of participation, and outcome measures. RESULTS: Most studies covered developmental transition points; none examined transitions as a variable for participation outcomes. Physical activity/exercise was the most common movement opportunity. Most publications used formal outcome measures of participation; others used observation or interviews. CONCLUSION: No publications adequately addressed the effect of movement opportunities on participation during developmental transition points.

Leadership dynamics in musical groups: Quantifying effects of musical structure on directionality of influence in concert performance videos.

Music ensemble performance provides an ecologically valid context for investigating leadership dynamics in small group interactions. Musical texture, specifically the relative salience of simultaneously sounding ensemble parts, is a feature that can potentially alter leadership dynamics by introducing hierarchical relationships between individual parts. The present study extended previous work on quantifying interpersonal coupling in musical ensembles by examining the relationship between musical texture and leader-follower relations, operationalised as directionality of influence between co-performers' body motion in concert video recordings. It was hypothesised that the directionality of influence, indexed by Granger Causality, would be greater for 'homophonic' textures with a clear distinction between melody and accompaniment parts than for 'polyphonic' textures with less distinction between melody and accompaniment. This hypothesis was tested by using pose estimation algorithms to track instrumentalists' body movements in a string quartet and a clarinet quintet, and then applying Granger Causality analysis to their head motion to estimate directional influence between instrumentalist pairs for sections of the pieces that varied in texture. It was found that Granger Causality values were generally higher (indicating greater directionality of influence) for homophonic than polyphonic textures. Furthermore, considering melody and accompaniment instrument roles revealed more evidence for the melody instrument influencing accompanying instruments than vice versa, plus a high degree of directionality among accompanying instruments, in homophonic textures. These observed patterns of directional information flow in co-performer body motion are consistent with changing leader-follower relations depending on hierarchical relations between ensemble parts in terms of the relative salience of melodic material in the musical texture. The finding that automatic pose estimation can detect modulations of leadership dynamics in standard video recordings under naturalistic performance conditions has implications for investigating interpersonal coordination in large-scale music video datasets representing different cultural traditions, and for exploring nonverbal communication in group activities more generally.

Continuous Motion Intention Prediction Using sEMG for Upper-Limb Rehabilitation: A Systematic Review of Model-Based and Model-Free Approaches.

Upper limb functional impairments persisting after stroke significantly affect patients' quality of life. Precise adjustment of robotic assistance levels based on patients' motion intentions using sEMG signals is crucial for active rehabilitation. This paper systematically reviews studies on continuous prediction of upper limb single joints and multi-joint combinations motion intention using Model-Based (MB) and Model-Free (MF) approaches over the past decade, based on 186 relevant studies screened from six major electronic databases. The findings indicate ongoing challenges in terms of subject composition, algorithm robustness and generalization, and algorithm feasibility for practical applications. Moreover, it suggests integrating the strengths of both MB and MF approaches to improve existing algorithms. Therefore, future research should further explore personalized MB-MF combination methods incorporating deep learning, attention mechanisms, muscle synergy features, motor unit features, and closed-loop feedback to achieve precise, real-time, and long-duration prediction of multi-joint complex movements, while further refining the transfer learning strategy for rapid algorithm deployment across days and subjects. Overall, this review summarizes the current research status, significant findings, and challenges, aiming to inspire future research on predicting upper limb motion intentions based on sEMG.

Does arthroscopic or open washout in native knee septic arthritis result in superior post-operative function? A systematic review and meta-analysis of randomised controlled trials and observational studies.

AIMS: Septic arthritis (SA) of the native knee joint is associated with significant morbidity. This review compared post-operative functional outcomes (patient-reported outcome measures (PROMs) and range of movement (ROM)) following arthroscopic washout (AW) and open washout (OW) amongst adult patients with SA of the native knee. The need for further operative intervention was also considered. METHODS: Electronic databases of PubMed, MEDLINE, Embase, Cochrane, Web of Science and Scopus were searched between 16 February 2023 and 18 March 2023. Randomised controlled trials (RCTs) and comparative observational analytic studies comparing function (reflected in PROMs or ROM) at latest follow-up following AW and OW were included. A narrative summary was provided concerning post-operative PROMs. Pooled estimates for mean ROM and re-operation rates were conducted using the random-effects model. The risk of bias was assessed using the Cochrane risk-of-bias assessment tool-2 for RCTs and the Risk of Bias in Non-Randomized Studies of Interventions tool for observational analytic studies. RESULTS: Of 2580 retrieved citations, 7 articles (1 RCT and 6 cohort studies) met the inclusion criteria. Of these, five had some concerns/moderate risk of bias, and two had serious risk. There was a slight tendency for superior mean PROMs following AW compared with OW, but due to small effect sizes, this was unlikely clinically relevant. Additionally, the use of four different PROMs scales made direct comparisons impossible. AW was associated with superior ROM (mean difference 20.18° (95% CI 14.35, 26.02; p < 0.00001)), whilst there was a tendency for lower re-operation requirements following AW (OR 0.64, 95% CI 0.26, 1.57, p = 0.44). CONCLUSIONS: AW was associated with equivalent to superior post-operative function and lower requirement for further intervention compared with OW. Results need to be interpreted cautiously, taking into consideration the methodological and clinical heterogeneity of the included studies. SYSTEMATIC REVIEW REGISTRATION: PROSPERO 2022, CRD42022364062.

Towards Automating Personal Exercise Assessment and Guidance with Affordable Mobile Technology.

Physical activity (PA) offers many benefits for human health. However, beginners often feel discouraged when introduced to basic exercise routines. Due to lack of experience and personal guidance, they might abandon efforts or experience musculoskeletal injuries. Additionally, due to phenomena such as pandemics and limited access to supervised exercise spaces, especially for the elderly, the need to develop personalized systems has become apparent. In this work, we develop a monitored physical exercise system that offers real-time guidance and recommendations during exercise, designed to assist users in their home environment. For this purpose, we used posture estimation interfaces that recognize body movement using a computer or smartphone camera. The chosen pose estimation model was BlazePose. Machine learning and signal processing techniques were used to identify the exercise currently being performed. The performances of three machine learning classifiers were evaluated for the exercise recognition task, achieving test-set accuracy between 94.76% and 100%. The research methodology included kinematic analysis (KA) of five selected exercises and statistical studies on performance and range of motion (ROM), which enabled the identification of deviations from the expected exercise execution to support guidance. To this end, data was collected from 57 volunteers, contributing to a comprehensive understanding of exercise performance. By leveraging the capabilities of the BlazePose model, an interactive tool for patients is proposed that could support rehabilitation programs remotely.

Chest Wall Motion Model of Cardiac Activity for Radar-Based Vital-Sign-Detection System.

An increasing number of studies on non-contact vital sign detection using radar are now beginning to turn to data-driven neural network approaches rather than traditional signal-processing methods. However, there are few radar datasets available for deep learning due to the difficulty of acquiring and labeling the data, which require specialized equipment and physician collaboration. This paper presents a new model of heartbeat-induced chest wall motion (CWM) with the goal of generating a large amount of simulation data to support deep learning methods. An in-depth analysis of published CWM data collected by the VICON Infrared (IR) motion capture system and continuous wave (CW) radar system during respiratory hold was used to summarize the motion characteristics of each stage within a cardiac cycle. In combination with the physiological properties of the heartbeat, appropriate mathematical functions were selected to describe these movement properties. The model produced simulation data that closely matched the measured data as evaluated by dynamic time warping (DTW) and the root-mean-squared error (RMSE). By adjusting the model parameters, the heartbeat signals of different individuals were simulated. This will accelerate the application of data-driven deep learning methods in radar-based non-contact vital sign detection research and further advance the field.

Patient's Healthy-Limb Motion Characteristic-Based Assist-As-Needed Control Strategy for Upper-Limb Rehabilitation Robots.

The implementation of a progressive rehabilitation training model to promote patients' motivation efforts can greatly restore damaged central nervous system function in patients. Patients' active engagement can be effectively stimulated by assist-as-needed (AAN) robot rehabilitation training. However, its application in robotic therapy has been hindered by a simple determination method of robot-assisted torque which focuses on the evaluation of only the affected limb's movement ability. Moreover, the expected effect of assistance depends on the designer and deviates from the patient's expectations, and its applicability to different patients is deficient. In this study, we propose a control method with personalized treatment features based on the idea of estimating and mapping the stiffness of the patient's healthy limb. This control method comprises an interactive control module in the task-oriented space based on the quantitative evaluation of motion needs and an inner-loop position control module for the pneumatic swing cylinder in the joint space. An upper-limb endpoint stiffness estimation model was constructed, and a parameter identification algorithm was designed. The upper limb endpoint stiffness which characterizes the patient's ability to complete training movements was obtained by collecting surface electromyographic (sEMG) signals and human-robot interaction forces during patient movement. Then, the motor needs of the affected limb when completing the same movement were quantified based on the performance of the healthy limb. A stiffness-mapping algorithm was designed to dynamically adjust the rehabilitation training trajectory and auxiliary force of the robot based on the actual movement ability of the affected limb, achieving AAN control. Experimental studies were conducted on a self-developed pneumatic upper limb rehabilitation robot, and the results showed that the proposed AAN control method could effectively estimate the patient's movement needs and achieve progressive rehabilitation training. This rehabilitation training robot that simulates the movement characteristics of the patient's healthy limb drives the affected limb, making the intensity of the rehabilitation training task more in line with the patient's pre-morbid limb-use habits and also beneficial for the consistency of bilateral limb movements.

Sensor-Based Quantification of MDS-UPDRS III Subitems in Parkinson's Disease Using Machine Learning.

Wearable sensors could be beneficial for the continuous quantification of upper limb motor symptoms in people with Parkinson's disease (PD). This work evaluates the use of two inertial measurement units combined with supervised machine learning models to classify and predict a subset of MDS-UPDRS III subitems in PD. We attached the two compact wearable sensors on the dorsal part of each hand of 33 people with PD and 12 controls. Each participant performed six clinical movement tasks in parallel with an assessment of the MDS-UPDRS III. Random forest (RF) models were trained on the sensor data and motor scores. An overall accuracy of 94% was achieved in classifying the movement tasks. When employed for classifying the motor scores, the averaged area under the receiver operating characteristic values ranged from 68% to 92%. Motor scores were additionally predicted using an RF regression model. In a comparative analysis, trained support vector machine models outperformed the RF models for specific tasks. Furthermore, our results surpass the literature in certain cases. The methods developed in this work serve as a base for future studies, where home-based assessments of pharmacological effects on motor function could complement regular clinical assessments.