The role of muscle strength & activation patterns in patellofemoral pain

To investigate the extent to which quadriceps muscle activation and strength are responsible for patellofemoral pain. A pain on–off switch system synchronized with a force transducer and surface electromyography was utilized on 32 volunteer patellofemoral pain patients during maximal isometric and squat exercises. There were 26 patients out of the 32 tested who complained of pain during the squat or isometric test, of these 20 subjects presented a significant advantage for the vastus lateralis compared to the vastus medialis obliquis activation and 12 patients had decreased quadriceps strength of the symptomatic compared to the non symptomatic leg. All patients who demonstrated weak vastus medialis obliquis activation during the isometric exercise possessed the same symptoms during the squat. On the other hand, 9 patients who showed diminished vastus medialis obliquis activation during the squat displayed equal activation between the vastus medialis obliquis and the vastus lateralis during the isometric task. With regard to the timing for the onset of muscle activation, there were only 4 patients who had a difference (P=0.03) between the symptomatic (0.042s) and non-symptomatic legs (0.011s). Causes for patellofemoral pain vary and are not necessarily a result of quadriceps strength deficit or vastus medialis obliquis activation weakness. Patellofemoral pain patients who possess lower vastus medialis obliquis activation compared to the vastus lateralis do not necessarily have quadriceps weakness while patients presenting with quadriceps strength deficits do not always have an imbalance between vastus medialis obliquis and vastus lateralis activation

An Energy-Based Variational Model of Ferromagnetic Hysteresis for Finite Element Computations

This paper proposes a macroscopic model for ferromagnetic hysteresis that is well-suited for finite element implementation. The model is readily vectorial and relies on a consistent thermodynamic formulation. In particular, the stored magnetic energy and the dissipated energy are known at all times, and not solely after the completion of closed hysteresis loops as is usually the case. The obtained incremental formulation is variationally consistent, i.e., all internal variables follow from the minimization of a thermodynamic potential

Threats and Opportunities for the Clinical Investigation of High-risk Medical Devices in the Context of the New European Regulations

International audienceThis position paper analyses the threats from the current situation of the clinical investigation to the expectations of the new European regulations focusing on high risk medical devices (HRMDs). We present also some opportunities to improve the feasibility and quality of clinical investigation. In summary, investigation protocols of medical devices, advised and authorized by the competent authorities, are few and heterogenous. There is a lack of quality in the existing studies, a lack of methodological knowledge and consequently high expectations for assistance from those involved in the design of clinical study protocols on HRMD. Guidance that is specific to the different type of devices is missing. Adaptive designs, pragmatic trial, usability methods, computer modeling and real world data are gaining more and more traction for assessing the safety and performance of high risk medical devices from a regulatory view-point

Artificial Intelligence and Internet of Things for autonomous vehicles

Artificial Intelligence (AI) is a machine intelligence tool providing enormous possibilities for smart industrial revolution. It facilitates gathering relevant data/information, identifying the alternatives, choosing among alternatives, taking some actions, making a decision, reviewing the decision, and predicting smartly. On the other hand, Internet of Things (IoT) is the axiom of industry 4.0 revolution, including a worldwide infrastructure for collecting and processing of the data/information from storage, actuation, sensing, advanced services and communication technologies. The combination of high-speed, resilient, low-latency connectivity, and technologies of AI and IoT will enable the transformation towards fully smart Autonomous Vehicle (AV) that illustrate the complementary between real world and digital knowledge for industry 4.0. The purpose of this book chapter is to examine how the latest approaches in AI and IoT can assist in the search for the AV. It has been shown that human errors are the source of 90% of automotive crashes, and the safest drivers drive ten times better than the average [1]. The automated vehicle safety is significant, and users are requiring 1000 times smaller acceptable risk level. Some of the incredible benefits of AVs are: (1) increasing vehicle safety, (2) reduction of accidents, (3) reduction of fuel consumption, (4) releasing of driver time and business opportunities, (5) new potential market opportunities, and (6) reduced emissions and dust particles. However, AVs must use large-scale data/information from their sensors and devices