General movements (GMs) are part of the spontaneous movement repertoire and are present from early fetal life onwards up to age five months. GMs are connected to infants' neurological development and can be qualitatively assessed via the General Movement Assessment (GMA). In particular, between the age of three to five months, typically developing infants produce Fidgety Movements (FM) and their absence provides strong evidence for the presence of cerebral palsy (CP). To improve accessibility to the GMA, automated GMA solutions have been a key research area with proposed models becoming increasingly more accurate and interpretable. However, current models cannot gauge their ability to make decisions, which may lead to overconfident mistakes. To address this issue, we propose a Deep learning-based approach that not only classifies movements as fidgety or non-fidgety but also selectively abstains from classification when uncertain. Through two novel regularization losses, our model maintains a balanced coverage across the two movement types, which prevents bias toward an easy-to-classify subset of movements. We show that our proposed model learns to gauge its own confidence on movement classification, and our proposed regularization losses effectively ensure that the model maintains a similar confidence across movement types. We also show that the local movement abstentions have little impact on the video-level coverage and that relying on the most confident predictions improves the video-level performance.

People with Parkinson's Disease (PD) often experience progressively worsening gait, including changes in how they turn around, as the disease progresses. Existing clinical rating tools are not capable of capturing hour-by-hour variations of PD symptoms, as they are confined to brief assessments within clinic settings, leaving gait performance outside these controlled environments unaccounted for. Measuring turning angles continuously and passively is a component step towards using gait characteristics as sensitive indicators of disease progression in PD. This paper presents a deep learning-based approach to automatically quantify turning angles by extracting 3D skeletons from videos and calculating the rotation of hip and knee joints. We utilise advanced human pose estimation models, Fastpose and Strided Transformer, on a total of 1386 turning video clips from 24 subjects (12 people with PD and 12 healthy control volunteers), trimmed from a PD dataset of unscripted free-living videos in a home-like setting (Turn-REMAP). We also curate a turning video dataset, Turn-H3.6M, from the public Human3.6M human pose benchmark with 3D groundtruth, to further validate our method. Previous gait research has primarily taken place in clinics or laboratories evaluating scripted gait outcomes, but this work focuses on free-living home settings where complexities exist, such as baggy clothing and poor lighting. Due to difficulties in obtaining accurate groundtruth data in a free-living setting, we quantise the angle into the nearest bin 45° based on the manual labelling of expert clinicians. Our method achieves a turning calculation accuracy of 41.6%, a Mean Absolute Error (MAE) of 34.7°, and a weighted precision (WPrec) of 68.3% for Turn-REMAP. On Turn-H3.6M, it achieves an accuracy of 73.5%, an MAE of 18.5°, and a WPrec of 86.2%. This is the first work to explore the use of single monocular camera data to quantify turns by PD patients in a home setting. All data and models are publicly available, providing a baseline for turning parameter measurement to promote future PD gait research.

Typically developing infants, between the corrected age of 9-20 weeks, produce fidgety movements. These movements can be identified with the General Movement Assessment, but their identification requires trained professionals to conduct the assessment from video recordings. Since trained professionals are expensive and their demand may be higher than their availability, computer vision-based solutions have been developed to assist practitioners. However, most solutions to date treat the problem as a direct mapping from video to infant status, without modeling fidgety movements throughout the video. To address that, we propose to directly model infants' short movements and classify them as fidgety or non-fidgety. In this way, we model the explanatory factor behind the infant's status and improve model interpretability. The issue with our proposal is that labels for an infant's short movements are not available, which precludes us to train such a model. We overcome this issue with active learning. Active learning is a framework that minimizes the amount of labeled data required to train a model, by only labeling examples that are considered "informative" to the model. The assumption is that a model trained on informative examples reaches a higher performance level than a model trained with randomly selected examples. We validate our framework by modeling the movements of infants' hips on two representative cohorts: typically developing and at-risk infants. Our results show that active learning is suitable to our problem and that it works adequately even when the models are trained with labels provided by a novice annotator.

Parkinson's Disease (PD) is the second most common neurodegenerative disease and prevalence is increasing as populations age. However, because it remains difficult to sensitively evaluate symptom progression, testing of therapies which change the disease course has been hampered (and no such therapy is yet licensed). The more recent use of home monitoring systems provides the potential for providing granular assessment of symptom progression, specifically, tremor evaluation, by continuous monitoring using inertial measurement units. A significant barrier remains in obtaining the ground truth information to assess inference models in these non-laboratory environments. Given these limitations, in this paper we propose a machine learning tremor regression model and classifier heuristic that uses minimal annotations. We use a real-world dataset of 12 participants, where pairs of participants, one with PD and a healthy control, are monitored over a four-day period in a home environment with wrist-worn devices. Our approach leverages the accelerometer frequency-time representation. We evaluate the classifier heuristic on the control participants. The tremor score regression model is trained on the self-assessments of the participants. We show that the tremor classifier can achieve false positive rates (FPR) less than 0.001 (on average one false positive every five hours) on control participants. For a particular participant, we show that the machine learning regression model achieves a mean absolute error (MAE) of 0.46 as compared to a polynomial regression model (degree=3) with MAE of 0.99. This research is the first to provide a semi-supervised machine learning approach using self-report of symptoms to continually predict tremor scores for individuals with PD.

Parkinson's disease (PD) is a slowly progressive, debilitating neurodegenerative disease which causes motor symptoms including gait dysfunction. Motor fluctuations are alterations between periods with a positive response to levodopa therapy ("on") and periods marked by re-emergency of PD symptoms ("off") as the response to medication wears off. These fluctuations often affect gait speed and they increase in their disabling impact as PD progresses. To improve the effectiveness of current indoor localisation methods, a transformer-based approach utilising dual modalities which provide complementary views of movement, Received Signal Strength Indicator (RSSI) and accelerometer data from wearable devices, is proposed. A sub-objective aims to evaluate whether indoor localisation, including its in-home gait speed features (i.e. the time taken to walk between rooms), could be used to evaluate motor fluctuations by detecting whether the person with PD is taking levodopa medications or withholding them. To properly evaluate our proposed method, we use a free-living dataset where the movements and mobility are greatly varied and unstructured as expected in real-world conditions. 24 participants lived in pairs (consisting of one person with PD, one control) for five days in a smart home with various sensors. Our evaluation on the resulting dataset demonstrates that our proposed network outperforms other methods for indoor localisation. The sub-objective evaluation shows that precise room-level localisation predictions, transformed into in-home gait speed features, produce accurate predictions on whether the PD participant is taking or withholding their medications.

Fidgety movements occur in infants between the age of 9 to 20 weeks post-term, and their absence are a strong indicator that an infant has cerebral palsy. Prechtl's General Movement Assessment method evaluates whether an infant has fidgety movements, but requires a trained expert to conduct it. Timely evaluation facilitates early interventions, and thus computer-based methods have been developed to aid domain experts. However, current solutions rely on complex models or high-dimensional representations of the data, which hinder their interpretability and generalization ability. To address that we propose [Formula: see text], a method that detects fidgety movements and uses them towards an assessment of the quality of an infant's general movements. [Formula: see text] is true to the domain expert process, more accurate, and highly interpretable due to its fine-grained scoring system. The main idea behind [Formula: see text] is to specify signal properties of fidgety movements that are measurable and quantifiable. In particular, we measure the movement direction variability of joints of interest, for movements of small amplitude in short video segments. [Formula: see text] also comprises a strategy to reduce those measurements to a single score that quantifies the quality of an infant's general movements; the strategy is a direct translation of the qualitative procedure domain experts use to assess infants. This brings [Formula: see text] closer to the process a domain expert applies to decide whether an infant produced enough fidgety movements. We evaluated [Formula: see text] on the largest clinical dataset reported, where it showed to be interpretable and more accurate than many methods published to date.

In interdisciplinary spaces such as digital health, datasets that are complex to collect, require specialist facilities, and/or are collected with specific populations have value in a range of different sectors. In this study we collected a simulated free-living dataset, in a smart home, with 12 participants (six people with Parkinson’s, six carers). We explored their initial perceptions of the sensors through interviews and then conducted two data exploration workshops, wherein we showed participants the collected data and discussed their views on how this data, and other data relating to their Parkinson’s symptoms, might be shared across different sectors. We provide recommendations around how participants might be better engaged in considering data sharing in the early stages of research, and guidance for how research might be configured to allow for more informed data sharing practices in the future.

The absence or abnormality of fidgety movements of joints or limbs is strongly indicative of cerebral palsy in infants. Developing computer-based methods for assessing infant movements in videos is pivotal for improved cerebral palsy screening. Most existing methods use appearance-based features and are thus sensitive to strong but irrelevant signals caused by background clutter or a moving camera. Moreover, these features are computed over the whole frame, thus they measure gross whole body movements rather than specific joint/limb motion. Addressing these challenges, we develop and validate a new method for fidgety movement assessment from consumer-grade videos using human poses extracted from short clips. Human poses capture only relevant motion profiles of joints and limbs and are thus free from irrelevant appearance artifacts. The dynamics and coordination between joints are modeled using spatio-temporal graph convolutional networks. Frames and body parts that contain discriminative information about fidgety movements are selected through a spatio-temporal attention mechanism. We validate the proposed model on the cerebral palsy screening task using a real-life consumer-grade video dataset collected at an Australian hospital through the Cerebral Palsy Alliance, Australia. Our experiments show that the proposed method achieves the ROC-AUC score of 81.87%, significantly outperforming existing competing methods with better interpretability.