Benefits of Crank Moment Sonification in Cycling

AbstractIn sports, the provision of augmented feedback is an important means to accelerate learning of new movements. Traditionally, concurrent augmented feedback has been provided verbally or visually. However, more recent studies have shown sonification of data during the movement, i.e. the mapping of a measured variable to parameter of sound, can be very effective to learn temporal aspects of a movement or movement patterns. In this pilot study, it was investigated if learning of complex pushing-pulling action applied to clipless pedals of a cycling ergometer can be enhanced by sonification of the crank moment. Three novice and three experienced cyclists were invited to train a reference crank moment pattern for two consecutive days (a total of twelve training sessions of 60 s each). However, in contrast to the results found in studies on rowing, the applied sonification did not enhance learning compared to visual and verbal instruction only. The lack of learning might be due to an inappropriate sonification design, short training sessions or the high task complexity. Extended studies are needed to draw more significant conclusions

Анализ траектории движения конечности на основе данных с микромеханических датчиков

Представлено биотехническое устройство, предназначенное для регистрации и оценки физической нагрузки пациента, представляющее собой гантель, оснащенную платой с микропроцессором и инерциальными датчиками. Для восстановления траектории движения снаряда используются гироскоп и акселеромет

Smart Sensing Technologies for Personalised Coaching

People living in both developed and developing countries face serious health challenges related to sedentary lifestyles. It is therefore essential to find new ways to improve health so that people can live longer and can age well. With an ever-growing number of smart sensing systems developed and deployed across the globe, experts are primed to help coach people toward healthier behaviors. The increasing accountability associated with app- and device-based behavior tracking not only provides timely and personalized information and support but also gives us an incentive to set goals and to do more. This book presents some of the recent efforts made towards automatic and autonomous identification and coaching of troublesome behaviors to procure lasting, beneficial behavioral changes

Kinematics and Robot Design IV, KaRD2021

This volume collects the papers published on the special issue “Kinematics and Robot Design IV, KaRD2021” (https://www.mdpi.com/journal/robotics/special_issues/KaRD2021), which is the forth edition of the KaRD special-issue series, hosted by the open-access journal “MDPI Robotics”. KaRD series is an open environment where researchers can present their works and discuss all the topics focused on the many aspects that involve kinematics in the design of robotic/automatic systems. Kinematics is so intimately related to the design of robotic/automatic systems that the admitted topics of the KaRD series practically cover all the subjects normally present in well-established international conferences on “mechanisms and robotics”. KaRD2021, after the peer-review process, accepted 12 papers. The accepted papers cover some theoretical and many design/applicative aspects

Event-driven Middleware for Body and Ambient Sensor Applications

Continuing development of on-body and ambient sensors has led to a vast increase in sensor-based assistance and monitoring solutions. A growing range of modular sensors, and the necessity of running multiple applications on the sensor information, has led to an equally extensive increase in efforts for system development. In this work, we present an event-driven middleware for on-body and ambient sensor networks allowing multiple applications to define information types of their interest in a publish/subscribe manner. Incoming sensor data is hereby transformed into the required data representation which lifts the burden of adapting the application with respect to the connected sensors off the developer's shoulders. Furthermore, an unsupervised on-the-fly reloading of transformation rules from a remote server allows the system's adaptation to future applications and sensors at run-time as well as reducing the number of connected sensors. Open communication channels distribute sensor information to all interested applications. In addition to that, application-specific event channels are introduced that provide tailor-made information retrieval as well as control over the dissemination of critical information. The system is evaluated based on an Android implementation with transformation rules implemented as OSGi bundles that are retrieved from a remote web server. Evaluation shows a low impact of running the middleware and the transformation rules on a phone and highlights the reduced energy consumption by having fewer sensors serving multiple applications. It also points out the behavior and limits of the open and application-specific event channels with respect to CPU utilization, delivery ratio, and memory usage. In addition to the middleware approach, four (preventive) health care applications are presented. They take advantage of the mediation between sensors and applications and highlight the system's capabilities. By connecting body sensors for monitoring physical and physiological parameters as well as ambient sensors for retrieving information about user presence and interactions with the environment, full-fledged health monitoring examples for monitoring a user throughout the day are presented. Vital parameters are gathered from commercially available biosensors and the mediator device running both the middleware and the application is an off-the-shelf smart phone. For gaining information about a user's physical activity, custom-built body and ambient sensors are presented and deployed

Combining Sensors and Multibody Models for Applications in Vehicles, Machines, Robots and Humans

The combination of physical sensors and computational models to provide additional information about system states, inputs and/or parameters, in what is known as virtual sensing, is becoming increasingly popular in many sectors, such as the automotive, aeronautics, aerospatial, railway, machinery, robotics and human biomechanics sectors. While, in many cases, control-oriented models, which are generally simple, are the best choice, multibody models, which can be much more detailed, may be better suited to some applications, such as during the design stage of a new product

A Reconfigurable and wearable wireless sensor system and its case study in the training of hammer throwers

Wearable sensors have been popularly used in many applications with the development of computer science and engineering. However, wearables for biomechanical feedback in motor learning and training are still rare. Therefore, this thesis focuses on developing an efficient and cost-effective wireless sensor system through a case study on the hammer throw. The results have shown that the proposed reconfigurable and wearable system can implement real-time biomechanical feedback in the hammer-throw training. Furthermore, the experimental results suggest that various throw-control patterns could be identified by using one tension-sensor and two inertial measurement units (i.e., more superior practicality than 3D motion capture), indicating that the low-cost wearable system has potential to substitute the expensive 3D motion capture technology. The proposed system can be easily modified and applied to many other applications, including but not limited to healthcare, rehabilitation, and smart homes, etc.National Science and Engineering Research Council of Canada (NSERC

Characterizing the Effects of High-intensity Exercise on Balance and Gait under Dual-task Conditions in Parkinson’s Disease

Parkinson’s disease (PD) is a neurodegenerative disorder, characterized by four cardinal motor symptoms including bradykinesia, tremor, rigidity, and postural instability, and non-motor symptoms including cognitive impairment. Daily activities, such as walking and maintaining balance, are impacted due to impairments in motor function, and are further exacerbated with the addition of cognitive loading, or dual-tasking (DT). High-intensity exercise has demonstrated centrally-mediated improvements of PD symptoms, with additional positive effects on overall health. The goal of this project was to identify changes in dynamic balance recovery and gait function under conditions with and without increased cognitive load after a high-intensity exercise intervention in a PD population. Participants included people with PD who completed an eight-week cycling intervention (PDE), people with Parkinson’s disease who did not complete the intervention (PDC), and healthy age-matched controls (HC), with 14 subjects per group. In Aim 1, while participants underwent a series of destabilizing balance tests, the time taken to regain balance and the center of pressure movement during balance recovery were measured. The PDE group demonstrated greater improvement in balance recovery after exercise compared with the PDC group. In Aim 2, participants completed a series of gait and cognitive tasks, both separately and concurrently. Outcome measures included spatiotemporal and kinematic gait parameters of the lower and upper extremities. The PDE group demonstrated significant improvement in gait measures and DT abilities compared to PDC, while no changes were found in cognitive function for any group. The standard clinical methods of measuring motor function can be subjective, and may not capture subtle motor characteristics. Force plate and motion-capture technologies can provide detailed, objective outcome data, therefore improving the understanding of how exercise affects motor symptoms of Parkinson’s disease. The Motek Computer Assisted Rehabilitation Environment (CAREN) system at the Cleveland Clinic was used to create the testing environment and for data collection. These results of this project suggest global changes in motor function demonstrated by changes in balance recovery and lower and upper extremity gait function. Quantitative gait analysis has shown to be an important metric in assessing effectiveness of an exercise intervention in PD

Characterizing the Effects of High-intensity Exercise on Balance and Gait under Dual-task Conditions in Parkinson’s Disease

Parkinson’s disease (PD) is a neurodegenerative disorder, characterized by four cardinal motor symptoms including bradykinesia, tremor, rigidity, and postural instability, and non-motor symptoms including cognitive impairment. Daily activities, such as walking and maintaining balance, are impacted due to impairments in motor function, and are further exacerbated with the addition of cognitive loading, or dual-tasking (DT). High-intensity exercise has demonstrated centrally-mediated improvements of PD symptoms, with additional positive effects on overall health. The goal of this project was to identify changes in dynamic balance recovery and gait function under conditions with and without increased cognitive load after a high-intensity exercise intervention in a PD population. Participants included people with PD who completed an eight-week cycling intervention (PDE), people with Parkinson’s disease who did not complete the intervention (PDC), and healthy age-matched controls (HC), with 14 subjects per group. In Aim 1, while participants underwent a series of destabilizing balance tests, the time taken to regain balance and the center of pressure movement during balance recovery were measured. The PDE group demonstrated greater improvement in balance recovery after exercise compared with the PDC group. In Aim 2, participants completed a series of gait and cognitive tasks, both separately and concurrently. Outcome measures included spatiotemporal and kinematic gait parameters of the lower and upper extremities. The PDE group demonstrated significant improvement in gait measures and DT abilities compared to PDC, while no changes were found in cognitive function for any group. The standard clinical methods of measuring motor function can be subjective, and may not capture subtle motor characteristics. Force plate and motion-capture technologies can provide detailed, objective outcome data, therefore improving the understanding of how exercise affects motor symptoms of Parkinson’s disease. The Motek Computer Assisted Rehabilitation Environment (CAREN) system at the Cleveland Clinic was used to create the testing environment and for data collection. These results of this project suggest global changes in motor function demonstrated by changes in balance recovery and lower and upper extremity gait function. Quantitative gait analysis has shown to be an important metric in assessing effectiveness of an exercise intervention in PD