The body of evidence of late-life depression: the complex relationship between depressive symptoms, movement, dyspnea and cognition.

BACKGROUND: Physical symptoms play an important role in late-life depression and may contribute to residual symptomatology after antidepressant treatment. In this exploratory study, we examined the role of specific bodily dimensions including movement, respiratory functions, fear of falling, cognition, and physical weakness in older people with depression. METHODS: Clinically stable older patients with major depression within a Psychiatric Consultation-Liaison program for Primary Care underwent comprehensive assessment of depressive symptoms, instrumental movement analysis, dyspnea, weakness, activity limitations, cognitive function, and fear of falling. Network analysis was performed to explore the unique adjusted associations between clinical dimensions. RESULTS: Sadness was associated with worse turning and walking ability and movement transitions from walking to sitting, as well as with worse general cognitive abilities. Sadness was also connected with dyspnea, while neurovegetative depressive burden was connected with activity limitations. DISCUSSION: Limitations of motor and cognitive function, dyspnea, and weakness may contribute to the persistence of residual symptoms of late-life depression.

Does Smartphone Use Affect a Subsequent Swimming Training Session? Preliminary Results in Amateur Triathletes.

To date, the literature has failed to individuate a clear motivation for the performance decrement after a mental fatigue-inducing task. This study aimed to evaluate biomechanical and perceptual variables during a swimming training session in different mental fatigue states. Seven amateur triathletes watched a documentary, utilized a smartphone, or performed an AX-CPT for 45 min randomly on three different days. After, they performed a 15-min warm-up followed by 6 × 200 m at constant pre-set speed plus one 200 m at maximal effort. The mental fatigue status was assessed by the visual analog scale (VAS) and short-Stroop task results before, post-mental task, and post-swimming session. The biomechanical and motor coordination variables during swimming were assessed using five IMU sensors and video analysis. The heart rate and rate of perceived exertion were monitored during the task. No differences in biomechanical and perceptual variables were found between and within conditions. Higher mental fatigue was found only in the AX-CPT condition at post task by VAS. In this preliminary study, no changes in swimming biomechanics were highlighted by mental fatigue, but the warm-up performed may have counteracted its negative effects. Further studies are recommended.

Modulations of Cortical Power and Connectivity in Alpha and Beta Bands during the Preparation of Reaching Movements.

Planning goal-directed movements towards different targets is at the basis of common daily activities (e.g., reaching), involving visual, visuomotor, and sensorimotor brain areas. Alpha (8-13 Hz) and beta (13-30 Hz) oscillations are modulated during movement preparation and are implicated in correct motor functioning. However, how brain regions activate and interact during reaching tasks and how brain rhythms are functionally involved in these interactions is still limitedly explored. Here, alpha and beta brain activity and connectivity during reaching preparation are investigated at EEG-source level, considering a network of task-related cortical areas. Sixty-channel EEG was recorded from 20 healthy participants during a delayed center-out reaching task and projected to the cortex to extract the activity of 8 cortical regions per hemisphere (2 occipital, 2 parietal, 3 peri-central, 1 frontal). Then, we analyzed event-related spectral perturbations and directed connectivity, computed via spectral Granger causality and summarized using graph theory centrality indices (in degree, out degree). Results suggest that alpha and beta oscillations are functionally involved in the preparation of reaching in different ways, with the former mediating the inhibition of the ipsilateral sensorimotor areas and disinhibition of visual areas, and the latter coordinating disinhibition of the contralateral sensorimotor and visuomotor areas.

The Effects of a Wetsuit on Biomechanical, Physiological, and Perceptual Variables in Experienced Triathletes.

PURPOSE: Wetsuits have been shown to change swim biomechanics and, thus, increase performance, but not all athletes are comfortable with their use because of possible modifications in motor coordination. The aim of this study was to evaluate the effects of wetsuit use on biomechanical, physiological, and perceptual variables. METHODS: Eleven national- and international-level triathletes, familiar with wetsuit use, performed 7 × 200-m front crawl at constant preset speed twice, with and without a full wetsuit. The trunk incline (TI) and index of coordination (IdC) were measured stroke by stroke using video analysis. Stroke, breaths, and kick count, and timing (as breathing/kick action per arm-stroke cycle); stroke length (SL); and underwater length were analyzed using inertial-measurement-unit sensors. Heart rate (HR), rating of perceived exertion (RPE), and swimming comfort were monitored during the task. RESULTS: A lower TI; IdC; number of strokes, kicks, and breaths; HR; and RPE for each 200 m were found in wetsuit compared with swimsuit condition. Higher values of SL and underwater length were found in wetsuit, whereas no differences were found in swimming comfort and timing of kicks and breaths. An increase for swimsuit condition in number of strokes and breaths, HR, and RPE was found during the task compared with the first 200 m. CONCLUSION: Wetsuit use reduces TI and, thus, drag; increases propelling proficiency; and shows lower fatigability, without modifying motor coordination, compared with swimsuit use at the same speed. The use of a wetsuit during training sessions is recommended to increase comfort and the positive effects on performance.

A Comparison between High and Low Cuff Pressures on Muscle Oxygen Saturation and Recovery Responses Following Blood-Flow Restriction Resistance Exercise.

The aim of the study was to compare the recovery response and muscle oxygenation of a blood-flow restriction resistance exercise (BFR) session with high [HP: 80% of the arterial occlusion pressure (AOP)] and low cuff pressure (LP: 40% of AOP). Both exercise sessions included 4 sets to failure at the barbell preacher curl exercise. Twelve resistance trained men (27.4 ± 5.0 years; 83.5 ± 11.6 kg; 176.6 ± 7.0 cm) performed each protocol in a counterbalanced, randomized order. Maximal isometric force, muscle morphology and muscle soreness of the biceps brachii muscle were assessed at baseline, 15-min, 60-min and 24-h post each testing session. In addition, muscle oxygen saturation (SmO2) was assessed during each training session. A lower number of repetitions (p = 0.013) was detected in HP compared to LP. A lower SmO2 (p < 0.001) was detected in the recovery time between the sets in HP (mean: 47.6 ± 15.7%) compared to LP (mean: 68.9 ± 7.2%). No differences between the two trials (p > 0.05) were noted for isometric force, muscle architecture and soreness at any timepoint. Results indicate that, despite a high cuff pressure may induce a more hypoxic condition compared to a lower cuff pressure, recovery responses may not be affected.

Aquatic Therapy after Incomplete Spinal Cord Injury: Gait Initiation Analysis Using Inertial Sensors.

Populations with potential damage to somatosensory, vestibular, and visual systems or poor motor control are often studied during gait initiation. Aquatic activity has shown to benefit the functional capacity of incomplete spinal cord injury (iSCI) patients. The present study aimed to evaluate gait initiation in iSCI patients using an easy-to-use protocol employing four wearable inertial sensors. Temporal and acceleration-based anticipatory postural adjustment measures were computed and compared between dry-land and water immersion conditions in 10 iSCI patients. In the aquatic condition, an increased first step duration (median value of 1.44 s vs. 0.70 s in dry-land conditions) and decreased root mean squared accelerations for the upper trunk (0.39 m/s2 vs. 0.72 m/s2 in dry-land conditions) and lower trunk (0.41 m/s2 vs. 0.85 m/s2 in dry-land conditions) were found in the medio-lateral and antero-posterior direction, respectively. The estimation of these parameters, routinely during a therapy session, can provide important information regarding different control strategies adopted in different environments.

The Use of Wearable Sensors for Preventing, Assessing, and Informing Recovery from Sport-Related Musculoskeletal Injuries: A Systematic Scoping Review.

Wearable technologies are often indicated as tools that can enable the in-field collection of quantitative biomechanical data, unobtrusively, for extended periods of time, and with few spatial limitations. Despite many claims about their potential for impact in the area of injury prevention and management, there seems to be little attention to grounding this potential in biomechanical research linking quantities from wearables to musculoskeletal injuries, and to assessing the readiness of these biomechanical approaches for being implemented in real practice. We performed a systematic scoping review to characterise and critically analyse the state of the art of research using wearable technologies to study musculoskeletal injuries in sport from a biomechanical perspective. A total of 4952 articles were retrieved from the Web of Science, Scopus, and PubMed databases; 165 were included. Multiple study features-such as research design, scope, experimental settings, and applied context-were summarised and assessed. We also proposed an injury-research readiness classification tool to gauge the maturity of biomechanical approaches using wearables. Five main conclusions emerged from this review, which we used as a springboard to propose guidelines and good practices for future research and dissemination in the field.

Integrated Timing of Stroking, Breathing, and Kicking in Front-Crawl Swimming: A Novel Stroke-by-Stroke Approach Using Wearable Inertial Sensors.

Quantitative evaluation of synergic action among the different body segments is fundamental to swimming performance. The aim of the present study was to develop an easy-to-use tool for stroke-by-stroke evaluation of a swimmer's integrated timing of stroking, kicking, and breathing. Twelve swimmers were evaluated during one trial of 100 m front-crawl swimming at self-selected speed. Five three-axial inertial sensors were mounted on the head, wrists, and ankles. Algorithms for the wrist entry into the water, the lower limb beat during the downward action, and the exit/entry of the face from/into the water were developed. Temporal events identified by video-based technique, using one sagittal moving camera, were assumed as the gold standard. The performance was evaluated in terms of the root-mean-square error, 90th percentile of absolute error, coefficient of variation, Bland-Altman plots, and correlation analysis. Results of all temporal events showed high agreement with the gold standard, confirmed by a root-mean-square error of less than 0.05 s for absolute temporal parameters and less than 0.7% for the percentages of the stroke cycle duration, and with correlation coefficients higher than 0.856. The protocol proposed was not only accurate and reliable, but also user-friendly and as unobtrusive as possible for the swimmer, allowing a stroke-by-stroke analysis during the training session.

A Lightweight Multi-Scale Convolutional Neural Network for P300 Decoding: Analysis of Training Strategies and Uncovering of Network Decision.

Convolutional neural networks (CNNs), which automatically learn features from raw data to approximate functions, are being increasingly applied to the end-to-end analysis of electroencephalographic (EEG) signals, especially for decoding brain states in brain-computer interfaces (BCIs). Nevertheless, CNNs introduce a large number of trainable parameters, may require long training times, and lack in interpretability of learned features. The aim of this study is to propose a CNN design for P300 decoding with emphasis on its lightweight design while guaranteeing high performance, on the effects of different training strategies, and on the use of post-hoc techniques to explain network decisions. The proposed design, named MS-EEGNet, learned temporal features in two different timescales (i.e., multi-scale, MS) in an efficient and optimized (in terms of trainable parameters) way, and was validated on three P300 datasets. The CNN was trained using different strategies (within-participant and within-session, within-participant and cross-session, leave-one-subject-out, transfer learning) and was compared with several state-of-the-art (SOA) algorithms. Furthermore, variants of the baseline MS-EEGNet were analyzed to evaluate the impact of different hyper-parameters on performance. Lastly, saliency maps were used to derive representations of the relevant spatio-temporal features that drove CNN decisions. MS-EEGNet was the lightest CNN compared with the tested SOA CNNs, despite its multiple timescales, and significantly outperformed the SOA algorithms. Post-hoc hyper-parameter analysis confirmed the benefits of the innovative aspects of MS-EEGNet. Furthermore, MS-EEGNet did benefit from transfer learning, especially using a low number of training examples, suggesting that the proposed approach could be used in BCIs to accurately decode the P300 event while reducing calibration times. Representations derived from the saliency maps matched the P300 spatio-temporal distribution, further validating the proposed decoding approach. This study, by specifically addressing the aspects of lightweight design, transfer learning, and interpretability, can contribute to advance the development of deep learning algorithms for P300-based BCIs.

Acute Effects of a High Volume vs. High Intensity Bench Press Protocol on Electromechanical Delay and Muscle Morphology in Recreationally Trained Women.

The purpose of the present investigation was to compare the acute responses on muscle architecture, electromechanical delay (EMD) and performance following a high volume (HV: 5 sets of 10 reps at 70% of 1 repetition maximum (1RM)) and a high intensity (HI: 5 sets of 3 reps at 90% of 1RM) bench press protocol in women. Eleven recreationally trained women (age = 23.3 ± 1.8 y; body weight = 59.7 ± 6.0 kg; height = 164.0 ± 6.3 cm) performed each protocol in a counterbalanced randomized order. Muscle thickness of pectoral (PEC MT) and triceps muscles (TR MT) were collected prior to and 15 min post each trial. In addition, EMD of pectoral (PEC EMD) and triceps (TR EMD) muscles were calculated during isometric bench press maximum force tests performed at the same timepoints (IBPF). Significantly greater increases in PEC MT (p < 0.001) and TR MT (p < 0.001) were detected following HV compared to HI. PEC EMD showed a significantly greater increase following HV compared to HI (p = 0.039). Results of the present study indicate that the HV bench press protocol results in greater acute morphological and neuromuscular changes compared to a HI protocol in women. Evaluations of muscle morphology and electromechanical delay appear more sensitive to fatigue than maximum isometric force assessments.

Quantitative characterization of walking on sand inecological conditions: Speed, temporal segmentation, and variability.

BACKGROUND: Walking on compliant surfaces, on sand in particular, is now recommended for training in both elderlies and injured subjects/individuals, allowing to perform high intensity exercises (i.e. augmented energy expenditure) in safe conditions (i.e. minimizing the impact on the joints and the risk of fall). Nevertheless, despite the assessment of energetics of walking on sand, the quantitative biomechanical characterization of walking on sand in ecological conditions is largely lacking. RESEARCH QUESTION: Which is the effect of sand surface on gait speed, gait temporal segmentation and their variability as related to surface compliance in ecological condition? METHODS: Eighteen healthy adults were assessed while walking on solid ground, dry-, and wet sand in ecological conditions by means of wearable inertial sensors (Miniwave, Cometa s.r.l., Italy). The best performing algorithm for the segmentation of walking on sand was selected among 17 algorithms designed for solid ground. Gait timing (i.e. speed, temporal segmentation, variability) was analysed, for the first time, with respect to sand compliance, and compared to walking on solid ground. RESULTS: Self-selected speed on a 60 m distance increased when walking on sand with respect to solid ground (Median 1.02 m/s), with the highest speed on wet sand (Median 1.15 m/s). A stabilizing strategy on the uneven surface provided by sand was highlighted by i) increased stance and double support durations with respect to speed on wet sand, and ii) increased short-term variability of stride, corresponding to continual adjustments of the lower limbs due to shifting surface provided by sand. SIGNIFICANCE: This study represents the first step in the objective characterization of walking on compliant surfaces as sand, necessary for the definition of training and rehabilitative programs.

Arm-Stroke Descriptor Variability during 200-m Front Crawl Swimming.

The present study aimed to explore the variability of the arm-stroke temporal descriptors between and within laps during middle-distance swimming event using IMMUs. Eight male swimmers performed a 200-m maximum front-crawl in which the inter-lap and intra-lap variability of velocity, stroke rate, stroke-phases duration and arm-coordination index were measured through five units of IMMU. An algorithm computes the 3D coordinates of the wrist by means the IMMU orientation and the kinematic chain of upper arm biomechanical model, and it recognizes the start events of the four arm-stroke phases. Velocity and stroke rate had a mean value of 1.47 ± 0.10 m·s-1 and 32.94 ± 4.84 cycles·min-1, respectively, and a significant decrease along the 200-m (p < 0.001; η2 = 0.80 and 0.47). The end of each lap showed significantly lower stroke rate compared to the start and the middle segment (p < 0.05; η2 = 0.55). No other significant inter-lap and intra-lap differences were detected. The two main findings are: (i) IMMUs technology can be an effective solution to continuously monitor the temporal descriptors during the swimming trial; (ii) swimmers are able to keep stable their temporal technique descriptors in a middle-distance event, despite the decrease of velocity and stroke rate.

Effect of walking speed during gait in water of healthy elderly.

BACKGROUND: Walking in water (WW) is frequently used as an aquatic exercise in rehabilitation programs for the elderly. Understanding gait characteristics of WW is of primary importance to effectively design specific water-based rehabilitation programs. Moreover, as walking speed in water is reduced with a possible effect on gait parameters, the age- and environment-related changes during WW have to be investigated considering the effects of instantaneous walking speed. RESEARCH QUESTION: how do gait kinematic characteristics differ in healthy elderly between WW and on land walking condition (LW)? Do elderly show different walking patterns compared to young adults? Can these kinematic changes be accounted only by the different environment/age or are they also related to walking speed? METHODS: Nine healthy elderly participants (73.5 ±â€¯5.8 years) were acquired during walking in WW and LW at two different speeds. Kinematic parameters were assessed with waterproofed inertial magnetic sensors using a validated protocol. The influence of environment, age and walking speed on gait parameters was investigated with linear mixed models. RESULTS: Shorter stride distances and longer stride durations were observed in WW compared to LW. In the sagittal plane, hip and knee joint showed larger flexion in WW (>10deg over the whole stride and ∼28deg at foot strike, respectively). Furthermore, lower walking speeds and stride distances were observed in elderly compared to young adults. In the sagittal plane, a slightly more flexed hip joint and a less plantarflexed ankle joint (∼9 deg) were observed in the elderly. SIGNIFICANCE: The results showed the importance of assessing the walking speed during WW, as gait parameters can vary not only for the effect environment but also due to different walking speeds.

Timing estimation for gait in water from inertial sensor measurements: Analysis of the performance of 17 algorithms.

BACKGROUND AND OBJECTIVES: Walking in water is used for rehabilitation in different pathological conditions. For the characterization of gait alterations related to pathology, gait timing assessment is of primary importance. With the widespread use of inertial sensors, several algorithms have been proposed for gait timing estimation (i.e. gait events and temporal parameters) out of the water, while an assessment of their performance for walking in water is still missing. The purpose of the present study was to assess the performance in the temporal segmentation for gait in water of 17 algorithms proposed in the literature. METHODS: Ten healthy volunteers mounting 5 tri-axial inertial sensors (trunk, shanks and feet) walked on dry land and in water. Seventeen different algorithms were implemented and classified based on: 1) sensor position, 2) target variable, and 3) computational approach. Gait events identified from synchronized video recordings were assumed as reference. Temporal parameters were calculated from gait events. Algorithm performance was analysed in terms of sensitivity, positive predictive value, accuracy, and repeatability. RESULTS: For walking in water, all Trunk-based algorithms provided a sensitivity lower than 81% and a positive predictive value lower than 94%, as well as acceleration-based algorithms, independently from sensor location, with the exception of two Shank-based ones. Drop in algorithm sensitivity and positive predictive value was associated to significant differences in the stride pattern of the specific analysed variables during walking in water as compared to walking on dry land, as shown by the intraclass correlation coefficient. When using Shank- or Foot-based algorithms, gait events resulted delayed, but the delay was compensated in the estimate of Stride and Step time; a general underestimation of Stance- and overestimation of Swing-time was observed, with minor exceptions. CONCLUSION: Sensor position, target variable and computational approach determined different error distributions for different gait events and temporal parameters for walking in water. This work supports an evidence-based selection of the most appropriate algorithm for gait timing estimation for walking in water as related to the specific application, and provides relevant information for the design of new algorithms for the specific motor task.

Interpretable and lightweight convolutional neural network for EEG decoding: Application to movement execution and imagination.

Convolutional neural networks (CNNs) are emerging as powerful tools for EEG decoding: these techniques, by automatically learning relevant features for class discrimination, improve EEG decoding performances without relying on handcrafted features. Nevertheless, the learned features are difficult to interpret and most of the existing CNNs introduce many trainable parameters. Here, we propose a lightweight and interpretable shallow CNN (Sinc-ShallowNet), by stacking a temporal sinc-convolutional layer (designed to learn band-pass filters, each having only the two cut-off frequencies as trainable parameters), a spatial depthwise convolutional layer (reducing channel connectivity and learning spatial filters tied to each band-pass filter), and a fully-connected layer finalizing the classification. This convolutional module limits the number of trainable parameters and allows direct interpretation of the learned spectral-spatial​ features via simple kernel visualizations. Furthermore, we designed a post-hoc gradient-based technique to enhance interpretation by identifying the more relevant and more class-specific features. Sinc-ShallowNet was evaluated on benchmark motor-execution and motor-imagery datasets and against different design choices and training strategies. Results show that (i) Sinc-ShallowNet outperformed a traditional machine learning algorithm and other CNNs for EEG decoding; (ii) The learned spectral-spatial features matched well-known EEG motor-related activity; (iii) The proposed architecture performed better with a larger number of temporal kernels still maintaining a good compromise between accuracy and parsimony, and with a trialwise rather than a cropped training strategy. In perspective, the proposed approach, with its interpretative capacity, can be exploited to investigate cognitive/motor aspects whose EEG correlates are yet scarcely known, potentially characterizing their relevant features.

ISB recommendations on the reporting of intersegmental forces and moments during human motion analysis.

The International Society of Biomechanics (ISB) has charged this committee with development of a standard similar in scope to the kinematic standard proposed in Wu et al. (2002) and Wu et al. (2005). Given the variety of purposes for which intersegmental forces and moments are used in biomechanical research, it is not possible to recommend a particular set of analysis standards that will be acceptable in all applications. Instead, it is the purpose of this paper to recommend a set of reporting standards that will result in an understanding of the differences between investigations and the ability to reproduce the research. The end products of this standard are (1) a critical checklist that can be used during submission of manuscripts and abstracts to insure adequate description of methods, and (2) a web based visualization tool that can be used to alter the coordinate system, normalization technique and internal/external perspective of intersegmental forces and moments during walking and running so that the shape and magnitude of the curves can be compared to one's own data.

Inertial Sensors in Swimming: Detection of Stroke Phases through 3D Wrist Trajectory.

Monitoring the upper arm propulsion is a crucial task for swimmer performance. The swimmer indeed can produce displacement of the body by modulating the upper limb kinematics. The present study proposes an approach for automatically recognize all stroke phases through three-dimensional (3D) wrist's trajectory estimated using inertial devices. Inertial data of 14 national-level male swimmer were collected while they performed 25 m front-crawl trial at intensity range from 75% to 100% of their 25 m maximal velocity. The 3D coordinates of the wrist were computed using the inertial sensors orientation and considering the kinematic chain of the upper arm biomechanical model. An algorithm that automatically estimates the duration of entry, pull, push, and recovery phases result from the 3D wrist's trajectory was tested using the bi-dimensional (2D) video-based systems as temporal reference system. A very large correlation (r = 0.87), low bias (0.8%), and reasonable Root Mean Square error (2.9%) for the stroke phases duration were observed using inertial devices versus 2D video-based system methods. The 95% limits of agreement (LoA) for each stroke phase duration were always lower than 7.7% of cycle duration. The mean values of entry, pull, push and recovery phases duration in percentage of the complete cycle detected using 3D wrist's trajectory using inertial devices were 34.7 (± 6.8)%, 22.4 (± 5.8)%, 14.2 (± 4.4)%, 28.4 (± 4.5)%. The swimmer's velocity and arm coordination model do not affect the performance of the algorithm in stroke phases detection. The 3D wrist trajectory can be used for an accurate and complete identification of the stroke phases in front crawl using inertial sensors. Results indicated the inertial sensor device technology as a viable option for swimming arm-stroke phase assessment.

Analysis of the performance of 17 algorithms from a systematic review: Influence of sensor position, analysed variable and computational approach in gait timing estimation from IMU measurements.

BACKGROUND: The quantification of gait temporal parameters (i.e. step time, stance time) is crucial in human motion analysis and requires the accurate identification of gait events (i.e. heel strike, toe off). With the widespread use of inertial wearable sensors, many algorithms were proposed and applied for the purpose. Nevertheless, only few studies addressed the assessment of the actual performance of these algorithms, rather considering each proposed algorithm as a whole. RESEARCH QUESTION: How different implementation characteristics influence the assessment of gait events and temporal parameters from inertial sensor measures in terms of accuracy and repeatability? METHODS: Seventeen different algorithms were identified from a systematic review and classified based on: 1) sensor position, 2) target variable, 3) computational approach. The influence of these characteristics was analysed on walking data of 35 healthy volunteers mounting 5 tri-axial inertial sensors. Foot contact events identified by 2 force platforms were assumed as gold standard. Temporal parameters were calculated from gait events. Algorithm performance was analysed in terms of accuracy (error median value) and repeatability (error 25th and 75th percentile values). RESULTS: Shank- and foot-based algorithms performed better (in terms of accuracy and repeatability) in gait events detection and stance time estimation than lower trunk-based ones, while sensor position did not affect step estimate, given the error bias characteristics. Angular velocity-based algorithms performed significantly better than acceleration-based ones for toe off detection in terms of repeatability (68 ms and 102 ms, 25th-75th percentile error range, respectively) and, for heel strike detection, showed better repeatability (40 ms and 111 ms) and comparable accuracy (65 ms and 60 ms median error, respectively) than acceleration-based ones. The performance of different computational approaches varied depending on sensor positioning. SIGNIFICANCE: Present results support the selection of the proper algorithm for the estimation of gait events and temporal parameters in relation to the specific application.

A kinematic comparison between long-line and cross-court top spin forehand in competitive table tennis players.

The aim of the present study was to compare the biomechanical characteristics of the table tennis top spin shot when played cross-court (CC) or long-line (LL) in competitive table tennis players. Seven national level players respectively completed 10 long-line and 10 cross-court top spin shots responding to a standard ball machine. A stereophotogrammetric system was used to track body segments while executing the motion. Significantly more flexed right knee and elbow angles were measured at the moment of maximum velocity of the racket (MMV) in LL. In addition, significantly greater angles between the feet and the table and between the shoulders and the table at the MMV, indicated more pronounced rotation angles of the lower upper and upper-body in LL compared to CC with respect to the table. A higher inclination of the racket at the MMV was found in LL. The elbow flexion and the racket inclination may be associated to the direction of the shot. The present findings show that kinematic differences exist between the LL and the CC topspin forehand in competitive table tennis players. Coaches should be aware of these differences to adopt the optimal teaching strategies and to reproduce proper joint angles during training.

Trends Supporting the In-Field Use of Wearable Inertial Sensors for Sport Performance Evaluation: A Systematic Review.

Recent technological developments have led to the production of inexpensive, non-invasive, miniature magneto-inertial sensors, ideal for obtaining sport performance measures during training or competition. This systematic review evaluates current evidence and the future potential of their use in sport performance evaluation. Articles published in English (April 2017) were searched in Web-of-Science, Scopus, Pubmed, and Sport-Discus databases. A keyword search of titles, abstracts and keywords which included studies using accelerometers, gyroscopes and/or magnetometers to analyse sport motor-tasks performed by athletes (excluding risk of injury, physical activity, and energy expenditure) resulted in 2040 papers. Papers and reference list screening led to the selection of 286 studies and 23 reviews. Information on sport, motor-tasks, participants, device characteristics, sensor position and fixing, experimental setting and performance indicators was extracted. The selected papers dealt with motor capacity assessment (51 papers), technique analysis (163), activity classification (19), and physical demands assessment (61). Focus was placed mainly on elite and sub-elite athletes (59%) performing their sport in-field during training (62%) and competition (7%). Measuring movement outdoors created opportunities in winter sports (8%), water sports (16%), team sports (25%), and other outdoor activities (27%). Indications on the reliability of sensor-based performance indicators are provided, together with critical considerations and future trends.