Temporal Segmentation of Human Motion for Rehabilitation

Current physiotherapy practice relies on visual observation of patient movement for assessment and diagnosis. Automation of motion monitoring has the potential to improve accuracy and reliability, and provide additional diagnostic insight to the clinician, improving treatment quality, and patient progress. To enable automated monitoring, assessment, and diagnosis, the movements of the patient must be temporally segmented from the continuous measurements. Temporal segmentation is the process of identifying the starting and ending locations of movement primitives in a time-series data sequence. Most segmentation algorithms require training data, but a priori knowledge of the patient's movement patterns may not be available, necessitating the use of healthy population data for training. However, healthy population movement data may not generalize well to rehabilitation patients due to large differences in motion characteristics between the two demographics. In this thesis, four key contributions will be elaborated to enable accurate segmentation of patient movement data during rehabilitation. The first key contribution is the creation of a segmentation framework to categorize and compare different segmentation algorithms considering segment definitions, data sources, application specific requirements, algorithm mechanics, and validation techniques. This framework provides a structure for considering the factors that must be incorporated when constructing a segmentation and identification algorithm. The framework enables systematic comparison of different segmentation algorithms, provides the means to examine the impact of each algorithm component, and allows for a systematic approach to determine the best algorithm for a given situation. The second key contribution is the development of an online and accurate motion segmentation algorithm based on a classification framework. The proposed algorithm transforms the segmentation task into a classification problem by modelling the segment edge point directly. Given this formulation, a variety of feature transformation, dimensionality reduction and classifier techniques were investigated on several healthy and patient datasets. With proper normalization, the segmentation algorithm can be trained using healthy participant data and obtain high quality segments on patient data. Inter-participant and inter-primitive variability were assessed on a dataset of 30 healthy participants and 44 rehabilitation participants, demonstrating the generalizability and utility of the proposed approach for rehabilitation settings. The proposed approach achieves a segmentation accuracy of 83-100%. The third key contribution is the investigation of feature set generalizability of the proposed method. Nearly all segmentation techniques developed previously use a single sensor modality. The proposed method was applied to joint angles, electromyogram, motion capture, and force plate data to investigate how the choice of modality impacts segmentation performance. With proper normalization, the proposed method was shown to work with various input sensor types and achieved high accuracy on all sensor modalities examined. The proposed approach achieves a segmentation accuracy of 72-97%. The fourth key contribution is the development of a new feature set based on hypotheses about the optimality of human motion trajectory generation. A common hypothesis in human motor control is that human movement is generated by optimizing with respect to a certain criterion and is task dependent. In this thesis, a method to segment human movement by detecting changes to the optimization criterion being used via inverse trajectory optimization is proposed. The control strategy employed by the motor system is hypothesized to be a weighted sum of basis cost functions, with the basis weights changing with changes to the motion objective(s). Continuous time series data of movement is processed using a sliding fixed width window, estimating the basis weights of each cost function for each window by minimizing the Karush-Kuhn-Tucker optimality conditions. The quality of the cost function recovery is verified by evaluating the residual. The successfully estimated basis weights are averaged together to create a set of time varying basis weights that describe the changing control strategy of the motion and can be used to segment the movement with simple thresholds. The proposed algorithm is first demonstrated on simulation data and then demonstrated on a dataset of human subjects performing a series of exercise tasks. The proposed approach achieves a segmentation accuracy of 74-88%

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

The persistent shadow of the supermassive black hole of M 87

In April 2019, the Event Horizon Telescope (EHT) Collaboration reported the first-ever event-horizon-scale images of a black hole, resolving the central compact radio source in the giant elliptical galaxy M 87. These images reveal a ring with a southerly brightness distribution and a diameter of ∼42 μas, consistent with the predicted size and shape of a shadow produced by the gravitationally lensed emission around a supermassive black hole. These results were obtained as part of the April 2017 EHT observation campaign, using a global very long baseline interferometric radio array operating at a wavelength of 1.3 mm. Here, we present results based on the second EHT observing campaign, taking place in April 2018 with an improved array, wider frequency coverage, and increased bandwidth. In particular, the additional baselines provided by the Greenland telescope improved the coverage of the array. Multiyear EHT observations provide independent snapshots of the horizon-scale emission, allowing us to confirm the persistence, size, and shape of the black hole shadow, and constrain the intrinsic structural variability of the accretion flow. We have confirmed the presence of an asymmetric ring structure, brighter in the southwest, with a median diameter of 43.3−3.1+1.5 μas. The diameter of the 2018 ring is remarkably consistent with the diameter obtained from the previous 2017 observations. On the other hand, the position angle of the brightness asymmetry in 2018 is shifted by about 30° relative to 2017. The perennial persistence of the ring and its diameter robustly support the interpretation that the ring is formed by lensed emission surrounding a Kerr black hole with a mass ∼6.5 × 109 M⊙. The significant change in the ring brightness asymmetry implies a spin axis that is more consistent with the position angle of the large-scale jet

Automated Rehabilitation Exercise Motion Tracking

c ○ Jonathan Feng-Shun Lin 2012I hereby declare that I am the sole author of this thesis. This is a true copy of the thesis, including any required final revisions, as accepted by my examiners. I understand that my thesis may be made electronically available to the public. Current physiotherapy practice relies on visual observation of the patient for diagnosis and assessment. The assessment process can potentially be automated to improve accuracy and reliability. This thesis proposes a method to recover patient joint angles and automatically extract movement profiles utilizing small and lightweight body-worn sensors. Joint angles are estimated from sensor measurements via the extended Kalman filter (EKF). Constant-acceleration kinematics is employed as the state evolution model. The forward kinematics of the body is utilized as the measurement model. The state and measurement models are used to estimate the position, velocity and acceleration of each joint, updated based on the sensor inputs from inertial measurement units (IMUs). Additional jointlimitconstraintsareimposedtoreducedrift, andanautomatedapproachisdeveloped for estimating and adapting the process noise during on-line estimation

Human Pose Recovery for Rehabilitation Using Ambulatory Sensors

Abstract — In this paper, an approach for lower-leg pose recovery from ambulatory sensors is implemented and validated in a clinical setting. Inertial measurement units are attached to patients undergoing physiotherapy. The sensor data is combined with a kinematic model within an extended Kalman filter framework to perform joint angle estimation. Anthropometric joint limits and process noise adaptation are employed to improve the quality of the joint angle estimation. The proposed approach is tested on 7 patients following total hip or knee joint replacement surgery. The proposed approach achieves an average root-mean-square error of 0.12 radians at key poses. I

Segmenting Human Motion for Automated Rehabilitation Exercise Analysis

Abstract — This paper proposes an approach for the automated segmentation and identification of movement segments from continuous time series data of human movement, collected through motion capture of ambulatory sensors. The proposed approach uses a two stage identification and recognition process, based on velocity and stochastic modeling of each motion to be identified. In the first stage, motion segment candidates are identified based on a unique sequence of velocity features such as velocity peaks and zero velocity crossings. In the second stage, Hidden Markov models are used to accurately identify segment locations from the identified candidates. The approach is capable of on-line segmentation and identification, enabling interactive feedback in rehabilitation applications. The approach is validated on a rehabilitation movement dataset, and achieves a segmentation accuracy of 89%. I