Synchronisation of wearable inertial measurement units based on magnetometer data.

OBJECTIVES: Synchronisation of wireless inertial measurement units in human movement analysis is often achieved using event-based synchronisation techniques. However, these techniques lack precise event generation and accuracy. An inaccurate synchronisation could lead to large errors in motion estimation and reconstruction and therefore wrong analysis outputs. METHODS: We propose a novel event-based synchronisation technique based on a magnetic field, which allows sub-sample accuracy. A setup featuring Shimmer3 inertial measurement units is designed to test the approach. RESULTS: The proposed technique shows to be able to synchronise with a maximum offset of below 2.6 ms with sensors measuring at 100 Hz. The investigated parameters suggest a required synchronisation time of 8 s. CONCLUSIONS: The results indicate a reliable event generation and detection for synchronisation of wireless inertial measurement units. Further research should investigate the temperature changes that the sensors are exposed to during human motion analysis and their influence on the internal time measurement of the sensors. In addition, the approach should be tested using inertial measurement units from different manufacturers to investigate an identified constant offset in the accuracy measurements.

Dynamic Craniotomy With NuCrani Reversibly Expandable Cranial Bone Flap Fixation Plates: A Technical Report.

BACKGROUND: Dynamic craniotomy provides cranial decompression without bone flap removal along with avoidance of cranioplasty and reduced risks for complications. OBJECTIVE: To report the first clinical cases using a novel dynamic craniotomy bone flap fixation system. The NeuroVention NuCrani reversibly expandable cranial bone flap fixation plates provide dynamic bone flap movement to accommodate changes in intracranial pressure (ICP) after a craniotomy. METHODS: The reversibly expandable cranial bone flap fixation plates were used for management of cerebral swelling in a patient with a subdural hemorrhage after severe traumatic brain injury and another patient with a hemorrhagic stroke. RESULTS: Both cases had high ICP's which normalized immediately after the dynamic craniotomy. Progressive postoperative cerebral swelling was noted which was compensated by progressive outward bone flap migration thereby maintaining a normal ICP, and with resolution of the cerebral swelling, the plates retracted the bone flaps to an anatomic flush position. CONCLUSION: The reversibly expandable plates provide an unhinged cranial bone flap outward migration with an increase in ICP and retract the bone flap after resolution of brain swelling while also preventing the bone flap from sinking inside the skull.

Automatic Assessment of Functional Movement Screening Exercises with Deep Learning Architectures.

(1) Background: The success of physiotherapy depends on the regular and correct unsupervised performance of movement exercises. A system that automatically evaluates these exercises could increase effectiveness and reduce risk of injury in home based therapy. Previous approaches in this area rarely rely on deep learning methods and do not yet fully use their potential. (2) Methods: Using a measurement system consisting of 17 inertial measurement units, a dataset of four Functional Movement Screening exercises is recorded. Exercise execution is evaluated by physiotherapists using the Functional Movement Screening criteria. This dataset is used to train a neural network that assigns the correct Functional Movement Screening score to an exercise repetition. We use an architecture consisting of convolutional, long-short-term memory and dense layers. Based on this framework, we apply various methods to optimize the performance of the network. For the optimization, we perform an extensive hyperparameter optimization. In addition, we are comparing different convolutional neural network structures that have been specifically adapted for use with inertial measurement data. To test the developed approach, it is trained on the data from different Functional Movement Screening exercises and the performance is compared on unknown data from known and unknown subjects. (3) Results: The evaluation shows that the presented approach is able to classify unknown repetitions correctly. However, the trained network is yet unable to achieve consistent performance on the data of previously unknown subjects. Additionally, it can be seen that the performance of the network differs depending on the exercise it is trained for. (4) Conclusions: The present work shows that the presented deep learning approach is capable of performing complex motion analytic tasks based on inertial measurement unit data. The observed performance degradation on the data of unknown subjects is comparable to publications of other research groups that relied on classical machine learning methods. However, the presented approach can rely on transfer learning methods, which allow to retrain the classifier by means of a few repetitions of an unknown subject. Transfer learning methods could also be used to compensate for performance differences between exercises.

Deep Convolutional and LSTM Networks on Multi-Channel Time Series Data for Gait Phase Recognition.

With an ageing society comes the increased prevalence of gait disorders. The restriction of mobility leads to a considerable reduction in the quality of life, because associated falls increase morbidity and mortality. Consideration of gait analysis data often alters surgical recommendations. For that reason, the early and systematic diagnostic treatment of gait disorders can spare a lot of suffering. As modern gait analysis systems are, in most cases, still very costly, many patients are not privileged enough to have access to comparable therapies. Low-cost systems such as inertial measurement units (IMUs) still pose major challenges, but offer possibilities for automatic real-time motion analysis. In this paper, we present a new approach to reliably detect human gait phases, using IMUs and machine learning methods. This approach should form the foundation of a new medical device to be used for gait analysis. A model is presented combining deep 2D-convolutional and LSTM networks to perform a classification task; it predicts the current gait phase with an accuracy of over 92% on an unseen subject, differentiating between five different phases. In the course of the paper, different approaches to optimize the performance of the model are presented and evaluated.

[Dialectical Behaviour Therapy for adolescents (DBT-A)--a pilot study on the therapy of suicidal, parasuicidal, and self-injurious behaviour in female patients with a borderline disorder]. / Dialektisch-Behaviorale Therapie für Adoleszente (DBT-A)--Eine Pilotstudie zur Therapie von Suizidalität, Parasuizidalität und selbstverletzenden Verhaltensweisen bei Patientinnen mit Symptomen einer Borderlinestörung.

OBJECTIVES: In Germany suicide ranks as the second leading cause of death in adolescents. Risk factors for suicide are impulsive and self-injurious behaviour, depression, and conduct disorder. The main hypothesis of our study is that Dialectical Behavior Therapy (DBT) for adolescents is an effective method of treatment for these patients. METHODS: DBT was developed by Marsha Linehan specifically for the outpatient treatment of chronically parasuicidal female patients with a diagnosis of borderline personality disorder. Miller & Rathus modified DBT for use with adolescents (DBT-A). and our group adapted the DBT-A for use in an outpatient treatment setting in Germany. In a pre-post comparison, the efficacy of treatment was measured using standardized instruments (SCL-90-R, CBCL, YSR, ILK, CGI, etc.). RESULTS: In a pilot study of 12 adolescents, we found effect sizes between 1.1 and 2.9. During treatment, self-injurious behaviour declined significantly. Prior to entering therapy, 8 of the 12 patients had attempted suicide at least once. During treatment according to DBT-A there were no suicide attempts. CONCLUSIONS: These results are so promising that we are now planning a randomized, multi-centre study.

Vocal tremor reduction with deep brain stimulation: a preliminary report.

Vocal tremor has been a challenging problem for patients and their physicians. In some cases, it has been possible to lesson the symptom's tremor through medications and/or voice therapy. However, in most cases no good treatment has been available. Chronic stimulation of the thalamus has been successful in controlling tremors of the upper limb and other portions of the body. Our preliminary experience suggests that it may also be helpful in controlling vocal tremor.