If motion sounds: Movement sonification based on inertial sensor data

Within last years, movement sonification turned out to be an appropriate support for motor perception and motor control that can display physical motion in a very rich and direct way. But how should movement sonification be configured to support motor learning? The appropriate selection of movement parameters and their transformation into characteristic motion features is essential for an auditory display to become effective. In this paper, we introduce a real-time sonification framework for all common MIDI environments based on acceleration and orientation data from inertial sensors. Fundamental processing steps to transform motion information into meaningful sound will be discussed. The proposed framework of inertial motion capturing, kinematic parameter selection and possible kinematic acoustic mapping provides a basis for mobile real-time movement sonification which is a prospective powerful training tool for rehabilitation and sports and offers a broad variety of application possibilities.EU/EFRE/W2-8011866

Auditory coding of reaching space

Reaching movements are usually initiated by visual events and controlled visually and kinesthetically. Lately, studies have focused on the possible benefit of auditory information for localization tasks, and also for movement control. This explorative study aimed to investigate if it is possible to code reaching space purely by auditory information. Therefore, the precision of reaching movements to merely acoustically coded target positions was analyzed. We studied the efficacy of acoustically effect-based and of additional acoustically performance-based instruction and feedback and the role of visual movement control. Twenty-four participants executed reaching movements to merely acoustically presented, invisible target positions in three mutually perpendicular planes in front of them. Effector-endpoint trajectories were tracked using inertial sensors. Kinematic data regarding the three spatial dimensions and the movement velocity were sonified. Thus, acoustic instruction and real-time feedback of the movement trajectories and the target position of the hand were provided. The subjects were able to align their reaching movements to the merely acoustically instructed targets. Reaching space can be coded merely acoustically, additional visual movement control does not enhance reaching performance. On the basis of these results, a remarkable benefit of kinematic movement acoustics for the neuromotor rehabilitation of everyday motor skills can be assumed

Assessing the Value of Recreational Divers for Censusing Elasmobranchs

BACKGROUND: Around the world, researchers are using the observations and experiences of citizens to describe patterns in animal populations. This data is often collected via ongoing sampling or by synthesizing past experiences. Since elasmobranchs are relatively rare, obtaining data for broad-scale trend analysis requires high sampling effort. Elasmobranchs are also relatively large and conspicuous and therefore it may be possible to enlist recreational divers to collect data on their occurrence and relative abundance from daily dive activities. For this, however, a good understanding of the value of data collected by recreational divers is essential. METHODOLOGY/PRINCIPAL FINDINGS: Here, we explore the value of recreational divers for censusing elasmobranchs using a diverse set of data sources. First, we use a simulation experiment to explore detection rates of the roving diver technique, used by recreational divers, across a range of fish densities and speeds. Next, using a field survey, we show that inexperienced recreational divers detect and count elasmobranchs as well as experienced recreational divers. Finally, we use semi-structured interviews of recreational dive instructors to demonstrate the value of their recollections in terms of effort and their descriptions of spatial and temporal distributions of sharks in Thailand. CONCLUSIONS/SIGNIFICANCE: Overall, this study provides initial ground-work for using recreational divers for monitoring elasmobranch populations. If used appropriately, citizen-collected data may provide additional information that can be used to complement more standardized surveys and to describe population trends across a range of spatial and temporal scales. Due to the non-extractive nature of this data, recreational divers may also provide important insight into the success of conservation initiatives, such as shark sanctuaries and no-take zones

Mycobacteria Attenuate Nociceptive Responses by Formyl Peptide Receptor Triggered Opioid Peptide Release from Neutrophils

In inflammation, pain is regulated by a balance of pro- and analgesic mediators. Analgesic mediators include opioid peptides which are secreted by neutrophils at the site of inflammation, leading to activation of opioid receptors on peripheral sensory neurons. In humans, local opioids and opioid peptides significantly downregulate postoperative as well as arthritic pain. In rats, inflammatory pain is induced by intraplantar injection of heat inactivated Mycobacterium butyricum, a component of complete Freund's adjuvant. We hypothesized that mycobacterially derived formyl peptide receptor (FPR) and/or toll like receptor (TLR) agonists could activate neutrophils, leading to opioid peptide release and inhibition of inflammatory pain. In complete Freund's adjuvant-induced inflammation, thermal and mechanical nociceptive thresholds of the paw were quantified (Hargreaves and Randall-Selitto methods, respectively). Withdrawal time to heat was decreased following systemic neutrophil depletion as well as local injection of opioid receptor antagonists or anti-opioid peptide (i.e. Met-enkephalin, β-endorphin) antibodies indicating an increase in pain. In vitro, opioid peptide release from human and rat neutrophils was measured by radioimmunoassay. Met-enkephalin release was triggered by Mycobacterium butyricum and formyl peptides but not by TLR-2 or TLR-4 agonists. Mycobacterium butyricum induced a rise in intracellular calcium as determined by FURA loading and calcium imaging. Opioid peptide release was blocked by intracellular calcium chelation as well as phosphoinositol-3-kinase inhibition. The FPR antagonists Boc-FLFLF and cyclosporine H reduced opioid peptide release in vitro and increased inflammatory pain in vivo while TLR 2/4 did not appear to be involved. In summary, mycobacteria activate FPR on neutrophils, resulting in tonic secretion of opioid peptides from neutrophils and in a decrease in inflammatory pain. Future therapeutic strategies may aim at selective FPR agonists to boost endogenous analgesia

Effectiveness and safety of opicapone in Parkinson’s disease patients with motor fluctuations: the OPTIPARK open-label study

Background The efficacy and safety of opicapone, a once-daily catechol-O-methyltransferase inhibitor, have been established in two large randomized, placebo-controlled, multinational pivotal trials. Still, clinical evidence from routine practice is needed to complement the data from the pivotal trials. Methods OPTIPARK (NCT02847442) was a prospective, open-label, single-arm trial conducted in Germany and the UK under clinical practice conditions. Patients with Parkinson’s disease and motor fluctuations were treated with opicapone 50 mg for 3 (Germany) or 6 (UK) months in addition to their current levodopa and other antiparkinsonian treatments. The primary endpoint was the Clinician’s Global Impression of Change (CGI-C) after 3 months. Secondary assessments included Patient Global Impressions of Change (PGI-C), the Unified Parkinson’s Disease Rating Scale (UPDRS), Parkinson’s Disease Questionnaire (PDQ-8), and the Non-Motor Symptoms Scale (NMSS). Safety assessments included evaluation of treatment-emergent adverse events (TEAEs) and serious adverse events (SAEs). Results Of the 506 patients enrolled, 495 (97.8%) took at least one dose of opicapone. Of these, 393 (79.4%) patients completed 3 months of treatment. Overall, 71.3 and 76.9% of patients experienced any improvement on CGI-C and PGI-C after 3 months, respectively (full analysis set). At 6 months, for UK subgroup only (n = 95), 85.3% of patients were judged by investigators as improved since commencing treatment. UPDRS scores at 3 months showed statistically significant improvements in activities of daily living during OFF (mean ± SD change from baseline: − 3.0 ± 4.6, p < 0.0001) and motor scores during ON (− 4.6 ± 8.1, p < 0.0001). The mean ± SD improvements of − 3.4 ± 12.8 points for PDQ-8 and -6.8 ± 19.7 points for NMSS were statistically significant versus baseline (both p < 0.0001). Most of TEAEs (94.8% of events) were of mild or moderate intensity. TEAEs considered to be at least possibly related to opicapone were reported for 45.1% of patients, with dyskinesia (11.5%) and dry mouth (6.5%) being the most frequently reported. Serious TEAEs considered at least possibly related to opicapone were reported for 1.4% of patients. Conclusions Opicapone 50 mg was effective and generally well-tolerated in PD patients with motor fluctuations treated in clinical practice. Trial registration Registered in July 2016 at clinicaltrials.gov (NCT02847442)

Deep learning—Accelerating Next Generation Performance Analysis Systems?

Deep neural network architectures show superior performance in recognition and prediction tasks of the image, speech and natural language domains. The success of such multi-layered networks encourages their implementation in further application scenarios as the retrieval of relevant motion information for performance enhancement in sports. However, to date deep learning is only seldom applied to activity recognition problems of the human motion domain. Therefore, its use for sports data analysis might remain abstract to many practitioners. This paper provides a survey on recent works in the field of high-performance motion data and examines relevant technologies for subsequent deployment in real training systems. In particular, it discusses aspects of data acquisition, processing and network modeling. Analysis suggests the advantage of deep neural networks under difficult and noisy data conditions. However, further research is necessary to confirm the benefit of deep learning for next generation performance analysis systems