Detecting Epileptic Seizures Using Hand-Crafted and Automatically Constructed EEG Features.

Epileptic seizure detection aims to replace unreliable seizure diaries by a model that automatically detects seizures based on electroencephalography (EEG) sensors. However, developing such a model is difficult and time consuming as it requires manually searching for relevant features from complex EEG data. Domain experts may have a partial understanding of the EEG characteristics that indicate seizures, but this knowledge is often not sufficient to exhaustively enumerate all relevant features. To address this challenge, we investigate how automated feature construction may complement hand-crafted features for epileptic seizure detection. By means of an empirical comparison on a real-world seizure detection dataset, we evaluate the ability of automated feature construction to come up with new relevant features. We show that combining hand-crafted and automated features results in more accurate models compared to using hand-crafted features alone. Our findings suggest that future studies on developing EEG-based seizure detection models may benefit from features constructed using a combination of hand-crafted and automated feature engineering.

Hypercapnic signaling influences hypoxic signaling in the control of breathing in C57BL6 mice.

Interactions between hypoxic and hypercapnic signaling pathways, expressed as ventilatory changes occurring during and following a simultaneous hypoxic-hypercapnic gas challenge (HH-C) have not been determined systematically in mice. This study in unanesthetized male C57BL6 mice addressed the hypothesis that hypoxic (HX) and hypercapnic (HC) signaling events display an array of interactions indicative of coordination by peripheral and central respiratory mechanisms. We evaluated the ventilatory responses elicited by hypoxic (HX-C, 10%, O2, 90% N2), hypercapnic (HC-C, 5% CO2, 21%, O2, 90% N2), and HH-C (10% O2, 5%, CO2, 85% N2) challenges to determine whether ventilatory responses elicited by HH-C were simply additive of responses elicited by HX-C and HC-C, or whether other patterns of interactions existed. Responses elicited by HH-C were additive for tidal volume, minute ventilation and expiratory time, among others. Responses elicited by HH-C were hypoadditive of the HX-C and HC-C responses (i.e., HH-C responses were less than expected by simple addition of HX-C and HC-C responses) for frequency of breathing, inspiratory time and relaxation time, among others. In addition, end-expiratory pause increased during HX-C, but decreased during HC-C and HH-C, therefore showing that HC-C responses influenced the HX-C responses when given simultaneously. Return to room-air responses was additive for tidal volume and minute ventilation, among others, whereas they were hypoadditive for frequency of breathing, inspiratory time, peak inspiratory flow, apneic pause, inspiratory and expiratory drives, and rejection index. These data show that HX-C and HH-C signaling pathways interact with one another in additive and often hypoadditive processes.NEW & NOTEWORTHY We present data showing that the ventilatory responses elicited by a hypoxic gas challenge in male C57BL6 mice are markedly altered by coexposure to hypercapnic gas challenge with hypercapnic responses often dominating the hypoxic responses. These data suggest that hypercapnic signaling processes activated within brainstem regions, such as the retrotrapezoid nuclei, may directly modulate the signaling processes within the nuclei tractus solitarius resulting from hypoxic-induced increase in carotid body chemoreceptor input to these nuclei.

Perspective on "in the wild" movement analysis using machine learning.

Recent advances in wearable sensing and machine learning have created ample opportunities for "in the wild" movement analysis in sports, since the combination of both enables real-time feedback to be provided to athletes and coaches, as well as long-term monitoring of movements. The potential for real-time feedback is useful for performance enhancement or technique analysis, and can be achieved by training efficient models and implementing them on dedicated hardware. Long-term monitoring of movement can be used for injury prevention, among others. Such applications are often enabled by training a machine learned model from large datasets that have been collected using wearable sensors. Therefore, in this perspective paper, we provide an overview of approaches for studies that aim to analyze sports movement "in the wild" using wearable sensors and machine learning. First, we discuss how a measurement protocol can be set up by answering six questions. Then, we discuss the benefits and pitfalls and provide recommendations for effective training of machine learning models from movement data, focusing on data pre-processing, feature calculation, and model selection and tuning. Finally, we highlight two application domains where "in the wild" data recording was combined with machine learning for injury prevention and technique analysis, respectively.

Machine-learned-based prediction of lower extremity overuse injuries using pressure plates.

Although running has many benefits for both the physical and mental health, it also involves the risk of injuries which results in negative physical, psychological and economical consequences. Those injuries are often linked to specific running biomechanical parameters such as the pressure pattern of the foot while running, and they could potentially be indicative for future injuries. Previous studies focus solely on some specific type of running injury and are often only applicable to a gender or running-experience specific population. The purpose of this study is, for both male and female, first-year students, (i) to predict the development of a lower extremity overuse injury in the next 6 months based on foot pressure measurements from a pressure plate and (ii) to identify the predictive loading features. For the first objective, we developed a machine learning pipeline that analyzes foot pressure measurements and predicts whether a lower extremity overuse injury is likely to occur with an AUC of 0.639 and a Brier score of 0.201. For the second objective, we found that the higher pressures exerted on the forefoot are the most predictive for lower extremity overuse injuries and that foot areas from both the lateral and the medial side are needed. Furthermore, there are two kinds of predictive features: the angle of the FFT coefficients and the coefficients of the autoregressive AR process. However, these features are not interpretable in terms of the running biomechanics, limiting its practical use for injury prevention.

Differential Ratings of Perceived Exertion: Relationships With External Intensity and Load in Elite Men's Football.

PURPOSE: To examine the utility of differential ratings of perceived exertion (dRPE) for monitoring internal intensity and load in association football. METHODS: Data were collected from 2 elite senior male football teams during 1 season (N = 55). External intensity and load data (duration × intensity) were collected during each training and match session using electronic performance and tracking systems. After each session, players rated their perceived breathlessness and leg-muscle exertion. Descriptive statistics were calculated to quantify how often players rated the 2 types of rating of perceived exertion differently (dRPEDIFF). In addition, the association between dRPEDIFF and external intensity and load was examined. First, the associations between single external variables and dRPEDIFF were analyzed using a mixed-effects logistic regression model. Second, the link between dRPEDIFF and session types with distinctive external profiles was examined using the Pearson chi-square test of independence. RESULTS: On average, players rated their session perceived breathlessness and leg-muscle exertion differently in 22% of the sessions (range: 0%-64%). Confidence limits for the effect of single external variables on dRPEDIFF spanned across largely positive and negative values for all variables, indicating no conclusive findings. The analysis based on session type indicated that players differentiated more often in matches and intense training sessions, but there was no pattern in the direction of differentiation. CONCLUSIONS: The findings of this study provide no evidence supporting the utility of dRPE for monitoring internal intensity and load in football.

Can the Output of a Learned Classification Model Monitor a Person's Functional Recovery Status Post-Total Knee Arthroplasty?

Osteoarthritis is a common musculoskeletal disorder. Classification models can discriminate an osteoarthritic gait pattern from that of control subjects. However, whether the output of learned models (probability of belonging to a class) is usable for monitoring a person's functional recovery status post-total knee arthroplasty (TKA) is largely unexplored. The research question is two-fold: (I) Can a learned classification model's output be used to monitor a person's recovery status post-TKA? (II) Is the output related to patient-reported functioning? We constructed a logistic regression model based on (1) pre-operative IMU-data of level walking, ascending, and descending stairs and (2) 6-week post-operative data of walking, ascending-, and descending stairs. Trained models were deployed on subjects at three, six, and 12 months post-TKA. Patient-reported functioning was assessed by the KOOS-ADL section. We found that the model trained on 6-weeks post-TKA walking data showed a decrease in the probability of belonging to the TKA class over time, with moderate to strong correlations between the model's output and patient-reported functioning. Thus, the LR-model's output can be used as a screening tool to follow-up a person's recovery status post-TKA. Person-specific relationships between the probabilities and patient-reported functioning show that the recovery process varies, favouring individual approaches in rehabilitation.

Impact of Gender and Feature Set on Machine-Learning-Based Prediction of Lower-Limb Overuse Injuries Using a Single Trunk-Mounted Accelerometer.

Even though practicing sports has great health benefits, it also entails a risk of developing overuse injuries, which can elicit a negative impact on physical, mental, and financial health. Being able to predict the risk of an overuse injury arising is of widespread interest because this may play a vital role in preventing its occurrence. In this paper, we present a machine learning model trained to predict the occurrence of a lower-limb overuse injury (LLOI). This model was trained and evaluated using data from a three-dimensional accelerometer on the lower back, collected during a Cooper test performed by 161 first-year undergraduate students of a movement science program. In this study, gender-specific models performed better than mixed-gender models. The estimated area under the receiving operating characteristic curve of the best-performing male- and female-specific models, trained according to the presented approach, was, respectively, 0.615 and 0.645. In addition, the best-performing models were achieved by combining statistical and sports-specific features. Overall, the results demonstrated that a machine learning injury prediction model is a promising, yet challenging approach.

Pneumonia is a common and early complication of the Severe Alcohol Withdrawal Syndrome (SAWS).

BACKGROUND: Pneumonia (PNA) may complicate the Severe Alcohol Withdrawal Syndrome (SAWS), with ICU admission, mechanical ventilation (MV), prolonged length of stay (LOS), and adverse events. OBJECTIVES: To examine the onset, features and courses of PNA in patients with SAWS to aid management. METHODS: A 33 month contiguous review of SAWS and PNA was conducted at an urban public hospital. RESULTS: There were 279 episodes of Alcohol Withdrawal Syndrome (AWS) among 255 patients. Males predominated (91%) with a mean age of 45.8 years (range 23-73), of whom 31% (87/279) developed SAWS with ICU management. Direct ICU admission occurred for 62 patients; 25 were transferred for delirium, seizures, escalating sedation, PNA or other complications. PNA was identified for 34 ICU direct admissions and 13 ward patients. Ten transfers to the ICU also developed PNA for an ICU total of 44/87 (51%), of whom 82% (36/44) required MV. Another 10 ICU patients without PNA received MV for high dose sedation or respiratory failure. Most ICU patients (72/87 (83%)), including all with MV, required IV infusion of sedation. MV prolonged LOS, but LOS for PNA with MV was similar to all MV. ICU transfers had longer LOS with greater use of MV than direct admits (p<0.05). PNA was identified before ICU admission or transfer for 73% (32/44 (p<0.05)), and usually before intubation. Most PNA was Community Acquired Pneumonia (CAP) with P. Pneumoniae frequently cultured. CONCLUSIONS: PNA with SAWS is predominately CAP and occurs early. Focused ICU admission with respiratory support are priorities of initial management.

Alcohol Withdrawal Syndrome in Women vs Men: Analysis of 1496 Cases at a Single Site.

BACKGROUND: Studies of alcohol withdrawal syndrome indicate a higher prevalence in men than in women. However, it is unknown how the condition differs between the sexes. OBJECTIVE: To assess alcohol withdrawal syndrome in women versus men at a single site. METHODS: All cases of alcohol withdrawal syndrome at a public hospital from 2010 to 2014 were reviewed retrospectively. For all 1496 episodes, age, sex, and admission to a general care unit (ward) versus the medical intensive care unit were ascertained, along with patient survival. A detailed analysis was performed of 437 cases: all 239 patients admitted to the medical intensive care unit, all 99 female patients admitted to the ward, and 99 randomly selected male patients admitted to the ward. Also analyzed were administration of benzodiazepines, disease course, length of stay, and complications. RESULTS: Men accounted for 92% of all cases (1378 of 1496; P < .001) and medical intensive care unit admissions (220 of 239; P < .05). Sixteen percent of both men and women were admitted to the medical intensive care unit. Men were older (mean age, 45.6 vs 43.9 years; P < .01), and women required more benzodiazepines. Similar rates of complications occurred in both sexes, although women had a higher rate of pancreatitis and men had higher rates of pneumonia, higher rates of sepsis, and longer stays. CONCLUSIONS: Men and women with alcohol withdrawal syndrome have similar complications, courses, and intensive care unit admission rates, although men are more prone to pneumonia and have longer stays.

Architecture- and Composition-Controlled Self-Assembly of Block Copolymers and Binary Mixtures With Crosslinkable Components: Chain Exchange Between Block Copolymer Nanoparticles.

Chain exchange behaviors in self-assembled block copolymer (BCP) nanoparticles (NPs) at room temperature are investigated through observations of structural differences between parent and binary systems of BCP NPs with and without crosslinked domains. Pairs of linear diblock or triblock, and branched star-like polystyrene-poly(2-vinylpyridine) (PS-PVP) copolymers that self-assemble in a PVP-selective mixed solvent into BCP NPs with definite differences in size and self-assembled morphology are combined by diverse mixing protocols and at different crosslinking densities to reveal the impact of chain exchange between BCP NPs. Clear structural evolution is observed by dynamic light scattering and AFM and TEM imaging, especially in a blend of triblock + star copolymer BCP NPs. The changes are ascribed to the chain motion inherent in the dynamic equilibrium, which drives the system to a new structure, even at room temperature. Chemical crosslinking of PVP corona blocks suppresses chain exchange between the BCP NPs and freezes the nanostructures at a copolymer crosslinking density (CLD) of ∼9%. This investigation of chain exchange behaviors in BCP NPs having architectural and compositional complexity and the ability to moderate chain motion through tailoring the CLD is expected to be valuable for understanding the dynamic nature of BCP self-assemblies and diversifying the self-assembled structures adopted by these systems. These efforts may guide the rational construction of novel polymer NPs for potential use, for example, as drug delivery platforms and nanoreactors.

Joint kinematics alone can distinguish hip or knee osteoarthritis patients from asymptomatic controls with high accuracy.

Osteoarthritis (OA) is one of the leading musculoskeletal disabilities worldwide, and several interventions intend to change the gait pattern in OA patients to more healthy patterns. However, an accessible way to follow up the biomechanical changes in a clinical setting is still missing. Therefore, this study aims to evaluate whether we can use biomechanical data collected from a specific activity of daily living to help distinguish hip OA patients from controls and knee OA patients from controls using features that potentially could be measured in a clinical setting. To achieve this goal, we considered three different classes of statistical models with different levels of data complexity. Class 1 is kinematics based only (clinically applicable), class 2 includes joint kinetics (semi-applicable under the condition of access to a force plate or prediction models), and class 3 uses data from advanced musculoskeletal modeling (not clinically applicable). We used a machine learning pipeline to determine which classification model was best. We found 100% classification accuracy for KneeOA-vs-Asymptomatic and 93.9% for HipOA-vs-Asymptomatic using seven features derived from the lumbar spine and hip kinematics collected during ascending stairs. These results indicate that kinematical data alone can distinguish hip or knee OA patients from asymptomatic controls. However, to enable clinical use, we need to validate if the classifier also works with sensor-based kinematical data and whether the probabilistic outcome of the logistic regression model can be used in the follow-up of patients with OA.

Evaluation of Automated Hypnogram Analysis on Multi-Scored Polysomnographies.

A new method for automated sleep stage scoring of polysomnographies is proposed that uses a random forest approach to model feature interactions and temporal effects. The model mostly relies on features based on the rules from the American Academy of Sleep Medicine, which allows medical experts to gain insights into the model. A common way to evaluate automated approaches to constructing hypnograms is to compare the one produced by the algorithm to an expert's hypnogram. However, given the same data, two expert annotators will construct (slightly) different hypnograms due to differing interpretations of the data or individual mistakes. A thorough evaluation of our method is performed on a multi-labeled dataset in which both the inter-rater variability as well as the prediction uncertainties are taken into account, leading to a new standard for the evaluation of automated sleep stage scoring algorithms. On all epochs, our model achieves an accuracy of 82.7%, which is only slightly lower than the inter-rater disagreement. When only considering the 63.3% of the epochs where both the experts and algorithm are certain, the model achieves an accuracy of 97.8%. Transition periods between sleep stages are identified and studied for the first time. Scoring guidelines for medical experts are provided to complement the certain predictions by scoring only a few epochs manually. This makes the proposed method highly time-efficient while guaranteeing a highly accurate final hypnogram.

Motion Sensor-Based Detection of Outlier Days Supporting Continuous Health Assessment for Single Older Adults.

The aging population has resulted in interest in remote monitoring of elderly individuals' health and well being. This paper describes a simple unsupervised monitoring system that can automatically detect if an elderly individual's pattern of presence deviates substantially from the recent past. The proposed system uses a small set of low-cost motion sensors and analyzes the produced data to establish an individual's typical presence pattern. Then, the algorithm uses a distance function to determine whether the individual's observed presence for each day significantly deviates from their typical pattern. Empirically, the algorithm is validated on both synthetic data and data collected by installing our system in the residences of three older individuals. In the real-world setting, the system detected, respectively, five, four, and one deviating days in the three locations. The deviating days detected by the system could result from a health issue that requires attention. The information from the system can aid caregivers in assessing the subject's health status and allows for a targeted intervention. Although the system can be refined, we show that otherwise hidden but relevant events (e.g., fall incident and irregular sleep patterns) are detected and reported to the caregiver.

Predicting gait events from tibial acceleration in rearfoot running: A structured machine learning approach.

BACKGROUND: Gait event detection of the initial contact and toe off is essential for running gait analysis, allowing the derivation of parameters such as stance time. Heuristic-based methods exist to estimate these key gait events from tibial accelerometry. However, these methods are tailored to very specific acceleration profiles, which may offer complications when dealing with larger data sets and inherent biological variability. RESEARCH QUESTION: Can a structured machine learning approach achieve a more accurate prediction of running gait event timings from tibial accelerometry, compared to the previously utilised heuristic approaches? METHODS: Force-based event detection acted as the criterion measure in order to assess the accuracy, repeatability and sensitivity of the predicted gait events. 3D tibial acceleration and ground reaction force data from 93 rearfoot runners were captured. A heuristic method and two structured machine learning methods were employed to derive initial contact, toe off and stance time from tibial acceleration signals. RESULTS: Both a structured perceptron model (median absolute error of stance time estimation: 10.00 ±â€¯8.73 ms) and a structured recurrent neural network model (median absolute error of stance time estimation: 6.50 ±â€¯5.74 ms) significantly outperformed the existing heuristic approach (median absolute error of stance time estimation: 11.25 ±â€¯9.52 ms). Thus, results indicate that a structured recurrent neural network machine learning model offers the most accurate and consistent estimation of the gait events and its derived stance time during level overground running. SIGNIFICANCE: The machine learning methods seem less affected by intra- and inter-subject variation within the data, allowing for accurate and efficient automated data output during rearfoot overground running. Furthermore offering possibilities for real-time monitoring and biofeedback during prolonged measurements, even outside the laboratory.

Towards the Monitoring of Functional Status in a Free-Living Environment for People with Hip or Knee Osteoarthritis: Design and Evaluation of the JOLO Blended Care App.

(1) Background: Joint loading is an important parameter in patients with osteoarthritis (OA). However, calculating joint loading relies on the performance of an extensive biomechanical analysis, which is not possible to do in a free-living situation. We propose the concept and design of a novel blended-care app called JOLO (Joint Load) that combines free-living information on activity with lab-based measures of joint loading in order to estimate a subject's functional status. (2) Method: We used an iterative design process to evaluate the usability of the JOLO app through questionnaires. The user interfaces that resulted from the iterations are described and provide a concept for feedback on functional status. (3) Results: In total, 44 people (20 people with OA and 24 health-care providers) participated in the testing of the JOLO app. OA patients rated the latest version of the JOLO app as moderately useful. Therapists were predominantly positive; however, their intention to use JOLO was low due to technological issues. (4) Conclusion: We can conclude that JOLO is promising, but further technological improvements concerning activity recognition, the development of personalized joint loading predictions and a more comfortable means to carry the device are needed to facilitate its integration as a blended-care program.

Accelerometer Based Data Can Provide a Better Estimate of Cumulative Load During Running Compared to GPS Based Parameters.

Running is a popular way to become or stay physically active and to maintain and improve one's musculoskeletal load tolerance. Despite the health benefits, running-related injuries affect millions of people every year and have become a substantial public health issue owing to the popularity of running. Running-related injuries occur when the musculoskeletal load exceeds the load tolerance of the human body. Therefore, it is crucial to provide runners with a good estimate of the cumulative loading during their habitual training sessions. In this study, we validated a wearable system to provide an estimate of the external load on the body during running and investigated how much of the cumulative load during a habitual training session is explained by GPS-based spatiotemporal parameters. Ground reaction forces (GRF) as well as 3D accelerations were registered in nine habitual runners while running on an instrumented treadmill at three different speeds (2.22, 3.33, and 4.44 m/s). Linear regression analysis demonstrated that peak vertical acceleration during running explained 80% of the peak vertical GRF. In addition, accelerometer-based as well as GPS-based parameters were registered during 498 habitual running session of 96 runners. Linear regression analysis showed that only 70% of the cumulative load (sum of peak vertical accelerations) was explained by duration, distance, speed, and the number of steps. Using a wearable device offers the ability to provide better estimates of cumulative load during a running program and could potentially serve as a better guide to progress safely through the program.

A Machine Learning Approach to Estimate Hip and Knee Joint Loading Using a Mobile Phone-Embedded IMU.

Hip osteoarthritis patients exhibit changes in kinematics and kinetics that affect joint loading. Monitoring this load can provide valuable information to clinicians. For example, a patient's joint loading measured across different activities can be used to determine the amount of exercise that the patient needs to complete each day. Unfortunately, current methods for measuring joint loading require a lab environment which most clinicians do not have access to. This study explores employing machine learning to construct a model that can estimate joint loading based on sensor data obtained solely from a mobile phone. In order to learn such a model, we collected a dataset from 10 patients with hip osteoarthritis who performed multiple repetitions of nine different exercises. During each repetition, we simultaneously recorded 3D motion capture data, ground reaction force data, and the inertial measurement unit data from a mobile phone attached to the patient's hip. The 3D motion and ground reaction force data were used to compute the ground truth joint loading using musculoskeletal modeling. Our goal is to estimate the ground truth loading value using only the data captured by the sensors of the mobile phone. We propose a machine learning pipeline for learning such a model based on the recordings of a phone's accelerometer and gyroscope. When evaluated for an unseen patient, the proposed pipeline achieves a mean absolute error of 29% for the left hip and 36% for the right hip. While our approach is a step in the direction of using a minimal number of sensors to estimate joint loading outside the lab, developing a tool that is accurate enough to be applicable in a clinical context still remains an open challenge. It may be necessary to use sensors at more than one location in order to obtain better estimates.

Tibial Acceleration-Based Prediction of Maximal Vertical Loading Rate During Overground Running: A Machine Learning Approach.

Ground reaction forces are often used by sport scientists and clinicians to analyze the mechanical risk-factors of running related injuries or athletic performance during a running analysis. An interesting ground reaction force-derived variable to track is the maximal vertical instantaneous loading rate (VILR). This impact characteristic is traditionally derived from a fixed force platform, but wearable inertial sensors nowadays might approximate its magnitude while running outside the lab. The time-discrete axial peak tibial acceleration (APTA) has been proposed as a good surrogate that can be measured using wearable accelerometers in the field. This paper explores the hypothesis that applying machine learning to time continuous data (generated from bilateral tri-axial shin mounted accelerometers) would result in a more accurate estimation of the VILR. Therefore, the purpose of this study was to evaluate the performance of accelerometer-based predictions of the VILR with various machine learning models trained on data of 93 rearfoot runners. A subject-dependent gradient boosted regression trees (XGB) model provided the most accurate estimates (mean absolute error: 5.39 ± 2.04 BW⋅s-1, mean absolute percentage error: 6.08%). A similar subject-independent model had a mean absolute error of 12.41 ± 7.90 BW⋅s-1 (mean absolute percentage error: 11.09%). All of our models had a stronger correlation with the VILR than the APTA (p < 0.01), indicating that multiple 3D acceleration features in a learning setting showed the highest accuracy in predicting the lab-based impact loading compared to APTA.

Predicting Future Perceived Wellness in Professional Soccer: The Role of Preceding Load and Wellness.

PURPOSE: The influence of preceding load and future perceived wellness of professional soccer players is unexamined. This paper simultaneously evaluates the external load (EL) and internal load (IL) for different time frames in combination with presession wellness to predict future perceived wellness using machine learning techniques. METHODS: Training and match data were collected from a professional soccer team. The EL was measured using global positioning system technology and accelerometry. The IL was obtained using the rating of perceived exertion multiplied by duration. Predictive models were constructed using gradient-boosted regression trees (GBRT) and one naive baseline method. The individual predictions of future wellness items (ie, fatigue, sleep quality, general muscle soreness, stress levels, and mood) were based on a set of EL and IL indicators in combination with presession wellness. The EL and IL were computed for acute and cumulative time frames. The GBRT model's performance on predicting the reported future wellness was compared with the naive baseline's performance by means of absolute prediction error and effect size. RESULTS: The GBRT model outperformed the baseline for the wellness items such as fatigue, general muscle soreness, stress levels, and mood. In addition, only the combination of EL, IL, and presession perceived wellness resulted in nontrivial effects for predicting future wellness. Including the cumulative load did not improve the predictive performances. CONCLUSIONS: The findings may indicate the importance of including both acute load and presession perceived wellness in a broad monitoring approach in professional soccer.

Use of wearable technology to quantify fall risk in psychogeriatric environments: a feasability study.

Fall incidents with elderly suffering from psychological pathologies, in combination with a comorbidity of clinical problems are highly prevalent. In our research setting, the psychiatric hospital OPZ in Geel, Belgium, 1790 fall incidents were recorded with 283 patients since 2013. The nature of the patients' profiles makes a valid, objective fall risk assessment very difficult; for them, instructions to perform the tests are difficult to understand and execute. Therefore, the currently used instruments are not suited for this complex situation. In this study we propose an alternative system for the assessment of fall risk for patients of a psychogeriatric ward. We also study the essential precautions needed for acceptance of wearables in this complex setting.We collected individual daily mean gait speeds of 17 patients at a psychogeriatric ward over a period of five months. We show that it is possible, using wearable technology, to measure individual gait speed. We also show that it is possible to have the wearable technology accepted by the target group. The results obtained so far are promising to use automatical gait measurement to correlate to the currently used risk assessment tests and to eventually replace these tests.