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An increasing number of studies across many research fields from biomedical engineering to finance are employing measures of entropy to quantify the regularity, variability or randomness of time series and image data. Entropy, as it relates to information theory and dynamical systems theory, can be estimated in many ways, with newly developed methods being continuously introduced in the scientific literature. Despite the growing interest in entropic time series and image analysis, there is a shortage of validated, open-source software tools that enable researchers to apply these methods. To date, packages for performing entropy analysis are often run using graphical user interfaces, lack the necessary supporting documentation, or do not include functions for more advanced entropy methods, such as cross-entropy, multiscale cross-entropy or bidimensional entropy. In light of this, this paper introduces EntropyHub , an open-source toolkit for performing entropic time series analysis in MATLAB, Python and Julia. EntropyHub (version 0.1) provides an extensive range of more than forty functions for estimating cross-, multiscale, multiscale cross-, and bidimensional entropy, each including a number of keyword arguments that allows the user to specify multiple parameters in the entropy calculation. Instructions for installation, descriptions of function syntax, and examples of use are fully detailed in the supporting documentation, available on the EntropyHub website– www.EntropyHub.xyz . Compatible with Windows, Mac and Linux operating systems, EntropyHub is hosted on GitHub, as well as the native package repository for MATLAB, Python and Julia, respectively. The goal of EntropyHub is to integrate the many established entropy methods into one complete resource, providing tools that make advanced entropic time series analysis straightforward and reproducible.

Parkinson’s disease (PD) is a progressive neurodegenerative disorder. Gait impairments are common among people with PD. Wearable sensor systems can be used for gait analysis by providing spatio-temporal parameters useful to investigate the progression of gait problems in Parkinson disease. However, various methods and tools with very high variability have been developed. The aim of this study is to review published articles of the last 10 years (from 2008 to 2018) concerning the application of wearable sensors to assess spatio-temporal parameters of gait in patients with PD. We focus on inertial sensors used for gait analysis in the clinical environment (i.e., we do not cover the use of inertial sensors to monitor walking or general activities at home, in unsupervised environments). Materials and Methods: Relevant articles were searched in the Medline database using Pubmed. Results and Discussion: Two hundred ninety-four articles were initially identified while searching the scientific literature regarding this topic. Thirty-six articles were selected and included in this review. Conclusion: Wearable motion sensors are useful, non-invasive, low-cost, and objective tools that are being extensively used to perform gait analysis on PD patients. Being able to diagnose and monitor the progression of PD patients makes wearable sensors very useful to evaluate clinical efficacy before and after therapeutic interventions. However, there is no uniformity in the use of wearable sensors in terms of: number of sensors, positioning, chosen parameters, and other characteristics. Future research should focus on standardizing the measurement setup and selecting which spatio-temporal parameters are the most informative to analyze gait in PD. These parameters should be provided as standard assessments in all studies to increase replicability and comparability of results.

Background Knee osteoarthritis patients may become physically inactive due to pain and functional limitations. Whether physical activity exerts a protective or harmful effect depends on the frequency, intensity, time and type (F.I.T.T.). The F.I.T.T. dimensions should therefore be assessed during daily life, which so far has hardly been feasible. Furthermore, physical activity should be assessed within subgroups of patients, as they might experience different activity limitations. Therefore, this study aimed to objectively describe physical activity, by assessing the F.I.T.T. dimensions, and sedentary behaviour of knee osteoarthritis patients during daily life. An additional goal was to determine whether activity events, based on different types and durations of physical activity, were able to discriminate between subgroups of KOA patients based on risk factors. Methods Clinically diagnosed knee osteoarthritis patients (according to American College of Rheumatology criteria) were monitored for 1 week with a tri-axial accelerometer. Furthermore, they performed three functional tests and completed the Knee Osteoarthritis Outcome Score. Physical activity levels were described for knee osteoarthritis patients and compared between subgroups. Results Sixty-one patients performed 7303 mean level steps, 319 ascending and 312 descending steps and 601 bicycle crank revolutions per day. Most waking hours were spent sedentary (61%), with 4.6 bouts of long duration (> 30 min). Specific events, particularly ascending and descending stairs/slopes, brief walking and sedentary bouts and prolonged walking bouts, varied between subgroups. Conclusions From this sample of KOA patients, the most common form of activity was level walking, although cycling and stair climbing activities occurred frequently, highlighting the relevance of distinguishing between these types of PA. The total active time encompassed a small portion of their waking hours, as they spent most of their time sedentary, which was exacerbated by frequently occurring prolonged bouts. In this study, event-based parameters, such as stair climbing or short bouts of walking or sedentary time, were found more capable of discriminating between subgroups of KOA patients compared to overall levels of PA and sedentary time. Thereby, subtle limitations in physical behaviour of KOA-subgroups were revealed, which might ultimately be targeted in rehabilitation programs. Trial registration German Clinical Trials Registry under ‘ DRKS00008735 ’ at 02.12.2015.

Background and Objectives: Outcome data from wearable devices are increasingly used in both research and clinics. Traditionally, a dedicated device is chosen for a given study or clinical application to collect outcome data as soon as the patient is included in a study or undergoes a procedure. The current study introduces a new measurement strategy, whereby patients’ own devices are utilized, allowing for both a pre-injury baseline measure and ability to show achievable results. Materials and Methods: Patients with a pre-existing musculoskeletal injury of the upper and lower extremity were included in this exploratory, proof-of-concept study. They were followed up for a minimum of 6 weeks after injury, and their wearable outcome data (from a smartphone and/or a body-worn sensor) were continuously acquired during this period. A descriptive analysis of the screening characteristics and the observed and achievable outcome patterns was performed. Results: A total of 432 patients was continuously screened for the study, and their screening was analyzed. The highest success rate for successful inclusion was in younger patients. Forty-eight patients were included in the analysis. The most prevalent outcome was step count. Three distinctive activity data patterns were observed: patients recovering, patients with slow or no recovery, and patients needing additional measures to determine treatment outcomes. Conclusions: Measuring outcomes in trauma patients with the Bring Your Own Device (BYOD) strategy is feasible. With this approach, patients were able to provide continuous activity data without any dedicated equipment given to them. The measurement technique is especially suited to particular patient groups. Our study’s screening log and inclusion characteristics can help inform future studies wishing to employ the BYOD design.

Purpose Previous investigations suggested that the geometry of the proximal femur may be related to osteoarthritis of the tibiofemoral joint and various patellofemoral joint conditions. This study aims to investigate the correlation between proximal and distal femoral geometry. Such a correlation could aid our understanding of patient complications after total knee arthroplasty (TKA) and be of benefit for further development of kinematic approaches in TKA. Methods CT scans of 60 subjects (30 males, 30 females) were used to identify anatomical landmarks to calculate anatomical parameters of the femur, including the femoral neck anteversion angle, neck–shaft angle (NSA), mediolateral offset (ML-offset), condylar twist angle (CTA), trochlear sulcus angle (TSA) and medial/lateral trochlear inclination angles (MTIA/LTIA). Correlation analyses were carried out to assess the relationship between these parameters, and the effect of gender was investigated. Results The CTA, TSA and LTIA showed no correlation with any proximal parameter. The MTIA was correlated with all three proximal parameters, mostly with the NSA and ML-offset. Per 5° increase in NSA, the MTIA was 2.1° lower ( p  < 0.01), and for every 5 mm increase in ML-offset, there was a 2.6° increase in MTIA ( p  < 0.01). These results were strongest and statistically significant in females and not in males and were independent of length and weight. Conclusions Proximal femoral geometry is distinctively linked with trochlear morphology. In order to improve knowledge on the physiological kinematics of the knee joint and to improve the concept of kinematic knee replacement, the proximal femur seems to be a factor of clinical importance. Level of evidence III.

Postoperative shoulder activity after proximal humerus fracture treatment could influence the outcomes of osteosynthesis and may depend on the rehabilitation protocol. This multi-centric prospective study aimed at evaluating the feasibility of continuous shoulder activity monitoring over the first six postoperative weeks, investigating potential differences between two different rehabilitation protocols. Shoulder activity was assessed with pairs of accelerometer-based trackers during the first six postoperative weeks in thirteen elderly patients having a complex proximal humerus fracture treated with a locking plate. Shoulder angles and elevation events were evaluated over time and compared between the two centers utilizing different standard rehabilitation protocols. The overall mean shoulder angle ranged from 11° to 23°, and the number of daily elevation events was between 547 and 5756. Average angles showed longitudinal change <5° over 31 ± 10 days. The number of events increased by 300% on average. Results of the two clinics exhibited no characteristic differences for shoulder angle, but the number of events increased only for the site utilizing immediate mobilization. In addition to considerable inter-patient variation, not the mean shoulder angle but the number of elevations events increased markedly over time. Differences between the two sites in number of daily events may be associated with the different rehabilitation protocols.

The aim of this study was to describe the physical activity profiles, in patients with stroke, Parkinson’s disease, multiple sclerosis and rheumatoid arthritis and to investigate the association between physical activity and functional performance. Physical activity profiles were conducted using tri-axial accelerometers and functional performance was examined by the “Six-Spot Step Test” and the “Timed Up and Go”. Patients daily performed 5896 ± 3176 steps with an average cadence of 88.3 ± 11.1, 368 ± 418 inclined walking steps and 50 ± 16 sit-stand transfers. Daily activity was modestly explained by functional performance. The activity profiles showed a large variance in activity parameters and results suggest that activity parameters and the two functional performance tests are different constructs.

Observations from laboratory-based gait analysis are difficult to extrapolate to real-world environments where gait behavior is modulated in response to complex environmental conditions and surface profiles. Inertial measurement units (IMUs) permit real-world gait analysis; however, automatic detection of surfaces encountered remains largely unexplored. The aims of this study are to quantify for machine learning models the effect of (1) random and subject-wise data splitting and (2) sensor location and count on surface classification performance. Thirty participants walked on nine surface conditions (flat-even, slope-up, slope-down, stairs-up, stairs-down, cobblestone, grass, banked-left, banked-right) wearing IMUs (wrist, trunk, bilateral thighs, bilateral shanks). Data were separated into gait cycles, normalized to 101 samples, and spilt into train and test sets (85 and 15%, respectively). For random splitting, trials were randomly assigned to the train or test set. In subject-wise splitting, all trials from 4 random participants were selected for testing. Linear discriminant analysis extracted features from the IMUs. Features were delivered to a neural network. F1-score evaluated model performance. Models achieved F1 scores of 0.96 and 0.78 using random and subject-wise splitting, respectively. Random splitting performance was mainly invariant to sensor location/count; however, subject-wise splitting showed best performance using lower-limb sensors. In general, stairs and sloped surfaces were easily predicted (F1 > 0.85) while banked surfaces were challenging, especially for subject-wise models (F1 ≈ 0.6). Neural networks can detect surfaces based on subtle changes in walking behavior captured by IMUs. Data splitting approaches and sensor location/count (subject-wise) have a non-negligible effect on model performance.

Background Joint replacement is an effective intervention for people with advanced arthritis, although there is an important minority of patients who do not improve post-operatively. There is a need for robust evidence on outcomes after surgery, but there are a number of measures that assess function after joint replacement, many of which lack any clear theoretical basis. The World Health Organisation has introduced the International Classification of Functioning, Disability and Health (ICF), which divides function into three separate domains: Impairment, activity limitations and participation restrictions. The aim of this study is to compare the properties and responsiveness of a selection of commonly used outcome tools that assess function, examine how well they relate to the ICF concepts, and to explore the changes in the measures over time. Methods/design Two hundred and sixty three patients listed for lower limb joint replacement at an elective orthopaedic centre have been recruited into this study. Participants attend the hospital for a research appointment prior to surgery and then at 3-months and 1-year after surgery. At each assessment time, function is assessed using a range of measures. Self-report function is assessed using the WOMAC, Aberdeen Impairment, Activity Limitation and Participation Restriction Measure, SF-12 and Measure Yourself Medical Outcome Profile 2. Clinician-administered measures of function include the American Knee Society Score for knee patients and the Harris Hip Score for hip patients. Performance tests include the timed 20-metre walk, timed get up and go, sit-to-stand-to-sit, step tests and single stance balance test. During the performance tests, participants wear an inertial sensor and data from motion analysis are collected. Statistical analysis will include exploring the relationship between measures describing the same ICF concepts, assessing responsiveness, and studying changes in measures over time. Discussion There are a range of tools that can be used to assess function before and after joint replacement, with little information about how these various measures compare in their properties and responsiveness. This study aims to provide this data on a selection of commonly used assessments of function, and explore how they relate to the ICF domains.