BackgroundAnalysis of lower limb exercises is traditionally completed with four distinct methods (i) 3D motion capture; (ii) depth-camera based systems (iii) visual analysis from a qualified exercise professional; (iv) self-assessment. Each method is associated with a number of limitations. ObjectiveThe aim of this systematic review is to synthesize and evaluate studies which have investigated the capacity for inertial measurement unit (IMU) technologies to assess movement quality in lower limb exercises. Data SourcesA systematic review of PubMed, ScienceDirect and Scopus was conducted. Study Eligibility CriteriaArticles written in English and published in the last 10 years which contained an IMU system for the analysis of repetition-based targeted lower limb exercises were included. Study Appraisal and Synthesis MethodsThe quality of included studies was measured using an adapted version of the STROBE assessment criteria for cross-sectional studies. The studies were categorised in to three groupings: exercise detection, movement classification or measurement validation. Each study was then qualitatively summarised. ResultsFrom the 2452 articles that were identified with the search strategies, 47 papers are included in this review. ConclusionsWearable inertial sensor systems for analysing lower limb exercises are a rapidly growing technology. Research over the past ten years has predominantly focused on validating measurements that the systems produce and classifying users' exercise quality. There have been very few user evaluation studies and no clinical trials in this field to date. Key PointsInertial measurement unit (IMU) systems have been extensively validated to successfully measure joint angle and temporal features during lower limb exercises.It is less understood if IMU systems can validly compute kinetic measures pertaining to lower limb exercises.IMU systems, which incorporate machine learning in to their data analysis pathways, have also been found to be effective in automated exercise detection and in classifying movement quality across a range of lower limb exercises.3

For centuries, cadaveric dissection has been the touchstone of anatomy education. It offers a medical student intimate access to his or her first patient. In contrast to idealized artisan anatomical models, it presents the natural variation of anatomy in fine detail. However, a new teaching construct has appeared recently in which artificial cadavers are manufactured through three-dimensional (3D) printing of patient specific radiological data sets. In this article, a simple powder based printer is made more versatile to manufacture hard bones, silicone muscles and perfusable blood vessels. The approach involves blending modern approaches (3D printing) with more ancient ones (casting and lost-wax techniques). These anatomically accurate models can augment the approach to anatomy teaching from dissection to synthesis of 3D-printed parts held together with embedded rare earth magnets. Vascular simulation is possible through application of pumps and artificial blood. The resulting arteries and veins can be cannulated and imaged with Doppler ultrasound. In some respects, 3D-printed anatomy is superior to older teaching methods because the parts are cheap, scalable, they can cover the entire age span, they can be both dissected and reassembled and the data files can be printed anywhere in the world and mass produced. Anatomical diversity can be collated as a digital repository and reprinted rather than waiting for the rare variant to appear in the dissection room. It is predicted that 3D printing will revolutionize anatomy when poly-material printing is perfected in the early 21st century.

The time series classification literature has expanded rapidly over the last decade, with many new classification approaches published each year. Prior research has mostly focused on improving the accuracy and efficiency of classifiers, with interpretability being somewhat neglected. This aspect of classifiers has become critical for many application domains and the introduction of the EU GDPR legislation in 2018 is likely to further emphasize the importance of interpretable learning algorithms. Currently, state-of-the-art classification accuracy is achieved with very complex models based on large ensembles (COTE) or deep neural networks (FCN). These approaches are not efficient with regard to either time or space, are difficult to interpret and cannot be applied to variable-length time series, requiring pre-processing of the original series to a set fixedlength. In this paper we propose new time series classification algorithms to address these gaps. Our approach is based on symbolic representations of time series, efficient sequence mining algorithms and linear classification models. Our linear models are as accurate as deep learning models but are more efficient regarding running time and memory, can work with variable-length time series and can be interpreted by highlighting the discriminative symbolic features on the original time series. We advance the state-of-the-art in time series classification by proposing new algorithms built using the following three key ideas: (1) Multiple resolutions of symbolic representations: we combine symbolic representations obtained using different parameters, rather than one fixed representation (e.g., multiple SAX representations); (2) Multiple domain representations: we combine symbolic representations in time (e.g., SAX) and frequency (e.g., SFA) domains, to be more robust across problem types; (3) Efficient navigation in a huge symbolic-words space: we extend a symbolic sequence classifier (SEQL) to work with multiple symbolic representations and use its greedy feature selection strategy to effectively filter the best features for each representation. We show that our multi-resolution multi-domain linear classifier (mtSS-SEQL+LR) achieves a similar accuracy to the state-of-the-art COTE ensemble, and to recent deep learning methods (FCN, ResNet), but uses a fraction of the time and memory required by either COTE or deep models. To further analyse the interpretability of our classifier, we present a case study on a human motion dataset collected by the authors. We discuss the accuracy, efficiency and interpretability of our proposed algorithms and release all the results, source code and data to encourage reproducibility.

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