Seglearn: A Python Package for Learning Sequences and Time Series

Seglearn is an open-source python package for machine learning time series or sequences using a sliding window segmentation approach. The implementation provides a flexible pipeline for tackling classification, regression, and forecasting problems with multivariate sequence and contextual data. This package is compatible with scikit-learn and is listed under scikit-learn Related Projects. The package depends on numpy, scipy, and scikit-learn. Seglearn is distributed under the BSD 3-Clause License. Documentation includes a detailed API description, user guide, and examples. Unit tests provide a high degree of code coverage

Helical Spring Design Optimization For Endoscopic Devices Using A Design-Of-Experiments And Response Surface Approach

Flexible endoscopes require reliable advancement and steerability of the device tip. Helical spring design is critical to both endoscopic steerability and cable function. To characterize the impact of geometric and material factors on endoscopic function, a parametric helical spring and a cable assembly was modelled using the finite element method and analyzed using a design-of-experiments approach. Individual input parameters (height, modulus (E), force, and width) and two interactions (pitch/width and pitch/height/E/width) were found to significantly impact the radius of curvature (a measure of steerability). The force and pitch/width interaction had negative effects, in contrast to positive effects from height, E, width and the pitch/height/E/width interaction. This information provides critical geometric and material information to guide helical spring design for optimized endoscopic steerability

T-Lymphocytes Enable Osteoblast Maturation via IL-17F during the Early Phase of Fracture Repair

While it is well known that the presence of lymphocytes and cytokines are important for fracture healing, the exact role of the various cytokines expressed by cells of the immune system on osteoblast biology remains unclear. To study the role of inflammatory cytokines in fracture repair, we studied tibial bone healing in wild-type and Rag1−/− mice. Histological analysis, µCT stereology, biomechanical testing, calcein staining and quantitative RNA gene expression studies were performed on healing tibial fractures. These data provide support for Rag1−/− mice as a model of impaired fracture healing compared to wild-type. Moreover, the pro-inflammatory cytokine, IL-17F, was found to be a key mediator in the cellular response of the immune system in osteogenesis. In vitro studies showed that IL-17F alone stimulated osteoblast maturation. We propose a model in which the Th17 subset of T-lymphocytes produces IL-17F to stimulate bone healing. This is a pivotal link in advancing our current understanding of the molecular and cellular basis of fracture healing, which in turn may aid in optimizing fracture management and in the treatment of impaired bone healing

Surgical process analysis identifies lack of connectivity between sequential fluoroscopic 2D alignment as a critical impediment in femoral intramedullary nailing

Purpose: Identifying key steps and barriers within complex and simple surgical procedures can be accomplished in a structured and rigorous manner using surgical process modeling. For lower extremity long bone fracture stabilization, the current standard of care is closed intramedullary (IM) nailing, which, despite its widespread use, is associated with challenges that greatly impact operative time and lead to the frustration of medical staff. The aim of this study was to identify challenging surgical steps in IM nailing and understand their underlying causation. Methods: Eight semi-structured interviews with staff orthopedic surgeons and eight detailed surgical observations were conducted to understand the surgical steps, challenges and adapted techniques used in IM nailing. Hierarchical decomposition was then utilized to structure the IM nailing surgical procedure into phases, steps and activities. Results: In the developed IM nailing surgical process model, the most challenging steps were identified as fracture reduction (75 %) and entry point selection (25 %), both of which were associated with high levels of frustration in the observed surgeries. Both of these steps utilize 2D fluoroscopic imaging to guide 3D alignment. Challenges arise when the alignment in one plane is lost while adjusting the alignment in the perpendicular plane. This leads to unpredictable repetition of activities which can be time-consuming and frustrating. Conclusion: Identifying the causation of surgical challenges in IM nailing through surgical process modeling forms a knowledge base that can be used to guide future improvements to techniques and surgical instrumentation

Evaluation of at-home physiotherapy: machine-learning prediction with smart watch inertial sensors

Aims: An objective technological solution for tracking adherence to at-home shoulder physiotherapy is important for improving patient engagement and rehabilitation outcomes, but remains a significant challenge. The aim of this research was to evaluate performance of machine-learning (ML) methodologies for detecting and classifying inertial data collected during in-clinic and at-home shoulder physiotherapy exercise. Methods: A smartwatch was used to collect inertial data from 42 patients performing shoulder physiotherapy exercises for rotator cuff injuries in both in-clinic and at-home settings. A two-stage ML approach was used to detect out-of-distribution (OOD) data (to remove non-exercise data) and subsequently for classification of exercises. We evaluated the performance impact of grouping exercises by motion type, inclusion of non-exercise data for algorithm training, and a patient-specific approach to exercise classification. Algorithm performance was evaluated using both in-clinic and at-home data. Results: The patient-specific approach with engineered features achieved the highest in-clinic performance for differentiating physiotherapy exercise from non-exercise activity (area under the receiver operating characteristic (AUROC) = 0.924). Including non-exercise data in algorithm training further improved classifier performance (random forest, AUROC = 0.985). The highest accuracy achieved for classifying individual in-clinic exercises was 0.903, using a patient-specific method with deep neural network model extracted features. Grouping exercises by motion type improved exercise classification. For at-home data, OOD detection yielded similar performance with the non-exercise data in the algorithm training (fully convolutional network AUROC = 0.919). Conclusion: Including non-exercise data in algorithm training improves detection of exercises. A patient-specific approach leveraging data from earlier patient-supervised sessions should be considered but is highly dependent on per-patient data quality. Cite this article: Bone Joint Res 2023;12(3):165–177

Automated quantitative microstructural analysis of metastatically involved vertebrae: effects of stereologic model and spatial resolution

Summary of background data: Preclinical models of spinal metastases allow for the application of micro-image based structural assessments, however, large data sets resulting from high resolution scanning motivate a need for robust automated analysis tools. Accurate assessment of changes in vertebral architecture, however, may depend both on the resolution of images acquired and the models used to represent the structural data. Objective: To apply a recently developed automated μCT based analysis tool to quantify the effect of diffuse metastatic disease on rat vertebral architecture at multiple resolutions. It was hypothesized that automated methods could accurately quantify differences in vertebral microstructure and that diffuse metastatic disease could be shown to have significant negative architectural effects on trabecular bone independent of stereologic model and resolution. Methods: μCT images acquired at 14 μm3 of healthy and metastatcially involved whole lumbar rat vertebrae were analyzed at high, medium and low (8.725, 17.45, and 34.9 μm3) resolutions using an automated algorithm to yield micro-structural measures of the trabecular centrum and cortical shell. The images analyzed at different resolutions were obtained via up/downsampling of the acquired image data. Trabecular thickness was evaluated with the Parfitt and Hildebrand models, and anisotropy was evaluated through calculation of mean intercept length. Results: Significant differences in microstructural parameters measured in comparing healthy and metastatically involved vertebrae were affected by resolution, however, relative anisotropy was maintained. The Parfitt and Hilderbrand models yielded similar structural differences between healthy and metastatic vertebrae, however, the Hildebrand model was limited due to segmentation accuracy required for its automated application. Conclusions: Differences in microstructural parameters generated through automated analysis at high resolution suggest that diffuse MT1 osteolytic destruction in whole rat vertebrae results primarily in loss of trabeculae in the metastatic vertebrae, as opposed to trabecular thinning. The sensitivity of the bony architectural parameters to resolution motivates the need for high resolution scanning or post-processing of images

Out-of-Distribution Detection of Human Activity Recognition with Smartwatch Inertial Sensors

Out-of-distribution (OOD) in the context of Human Activity Recognition (HAR) refers to data from activity classes that are not represented in the training data of a Machine Learning (ML) algorithm. OOD data are a challenge to classify accurately for most ML algorithms, especially deep learning models that are prone to overconfident predictions based on in-distribution (IIN) classes. To simulate the OOD problem in physiotherapy, our team collected a new dataset (SPARS9x) consisting of inertial data captured by smartwatches worn by 20 healthy subjects as they performed supervised physiotherapy exercises (IIN), followed by a minimum 3 h of data captured for each subject as they engaged in unrelated and unstructured activities (OOD). In this paper, we experiment with three traditional algorithms for OOD-detection using engineered statistical features, deep learning-generated features, and several popular deep learning approaches on SPARS9x and two other publicly-available human activity datasets (MHEALTH and SPARS). We demonstrate that, while deep learning algorithms perform better than simple traditional algorithms such as KNN with engineered features for in-distribution classification, traditional algorithms outperform deep learning approaches for OOD detection for these HAR time series datasets

Mesh morphing and response surface analysis: quantifying sensitivity of vertebral mechanical behavior

Vertebrae provide essential biomechanical stability to the skeleton. In this work novel morphing techniques were used to parameterize three aspects of the geometry of a specimen-specific finite element (FE) model of a rat caudal vertebra (process size, neck size, and end-plate offset). Material properties and loading were also parameterized using standard techniques. These parameterizations were then integrated within an RSM framework and used to produce a family of FE models. The mechanical behavior of each model was characterized by predictions of stress and strain. A metamodel was fit to each of the responses to yield the relative influences of the factors and their interactions. The direction of loading, offset, and neck size had the largest influences on the levels of vertebral stress and strain. Material type was influential on the strains, but not the stress. Process size was substantially less influential. A strong interaction was identified between dorsal-ventral offset and dorsal-ventral off-axis loading. The demonstrated approach has several advantages for spinal biomechanical analysis by enabling the examination of the sensitivity of a specimen to multiple variations in shape, and of the interactions between shape, material properties, and loading