The interest in using new technologies to obtain recordings of on-water kinetic variables for assessing the performance of elite sprint kayakers has increased over the last decades but systematic approaches are warranted to ensure the validity and reliability of these measures. This study has an innovative approach, and the aim was to develop a new versatile jig including reference force sensors for both the calibration and validation of mutual static and dynamic stroke forces as measured with instrumented paddles at the high force levels used in elite sprint kayaking. Methods: A jig was constructed using a modified gym weight stack and a frame consisting of aluminum profiles permitting a fastening of custom-made kayak paddle shaft and blade support devices with certified force transducers combined with a data acquisition system to record blade and hand forces during static (constant load) and dynamic conditions (by paddle stroke simulation). A linear motion path incorporating a ball-bearing equipped carriage with sensors for the measurement of vertical distance and horizontal displacement was attached to the frame for recordings of various position measures on the paddle. The jig design with all components is extensively described to permit replication. The procedures for assessing the accuracy of the jig force instrumentation are reported, and with one brand of instrumented paddle used as an example, methods are described for force calibration and validation during static and dynamic conditions. Results: The results illustrate that the measured force with the jig instrumentation was similar to the applied force, calculated from the applied accurate mass (within a -1.4 to 1.8% difference) and similar to the force as calculated from the applied mass with the weight stack (within a -0.57 to 1.16% difference). The jig was suitable for the calibration and validation of forces in a range relevant for elite sprint kayaking under both static and dynamic conditions. During static conditions with a force direction equal to the calibration conditions and a force range from 98 to 590 N, all values for the instrumented paddle were within a -3.4 to 3.0% difference from the jig sensor values and 28 of 36 values were within +/- 2%. During dynamic conditions with paddle stroke simulations at 60 and 100 strokes/min and a target peak force of 400 N, the common force variables as measured by the instrumented paddle were not significantly different from the same measures by the jig (values at 100 strokes/min: peak force; 406.9 +/- 18.4 vs. 401.9 +/- 17.2 N, mean force; 212.8 +/- 15.4 vs. 212.0 +/- 14.4 N, time to peak force; 0.17 +/- 0.02 vs. 0.18 +/- 0.02 s, force impulse; 90.8 +/- 11.2 vs. 90.5 +/- 10.8 Ns, impulse duration; 0.43 +/- 0.03 vs. 0.43 +/- 0.03 s). Conclusion: A novel jig with several new functions is presented that enables the calibration and validation of force measurements with instrumented paddles by providing standardized conditions for calibration and force validation during both static and dynamic conditions in a force range relevant to elite sprint kayaking.

Video is the most used tool for sport performance analysis as it provides a common reference point for the coach and the athlete. The problem with video is that it is a subjective tool. To overcome this, motion capture systems can used to get an objective 3D model of a per- son’s posture and motion, but only in laboratory settings. Unfortunately, many activities, such as most outdoor sports, cannot be captured in a lab without compromising the activity. In this paper, we propose to use an aerial drone system equipped with depth cameras, AI-based marker- less motion capture software to perform automatic skeleton tracking and real-time sports performance analysis of athletes. We experiment with off-the-shelf drone systems, miniaturized depth cameras, and commer- cially available skeleton tracking software to build a system for analyzing sports-related performance of athletes in their real settings. To make this a fully working system, we have conducted a few initial experiments and identified many issues that still needs to be addressed.

A quantitative wrist angular velocity to wrist-related disorders relation have been reported in hand intensive work. This velocity has been complicated to measure. A new sensors and smartphone method was developed and tested. The result indicate the prototype as a promising tool, which in the future may support researchers and practitioners in exposure quantification and risk assessment of hand intensive repetitive work tasks.

Today, the programming of a complete wearable sensor system requires writing code in different programming languages for the different parts of the systems, such as the wearable sensor platform itself, the gateway, the back-end server, and the client app. In this paper, we propose to use WebAssembly, which is a simple but powerful virtual machine standard already supported by all major web browsers. We show that it is possible to implement a WebAssembly interpreter for embedded systems, such as the Texas Instruments CC2652R system-on-chip and this enables the same code to execute in all parts of the systems. In our proof-of-concept implementation, we use Bluetooth low energy, which means that smartphones can communicate with and program our device without the need for special hardware. 

Bluetooth Low Energy (BLE) is currently the dominating wireless network solution for eHealth and sports. However, most BLE sensors require dedicated applications with limited development capabilities. This paper presents a method for rapid development of applications in distributed BLE IoT systems for eHealth and sports. The method is implemented as a JavaScript web framework based on HTML5 canvas, WebSocket and Web Bluetooth APIs. This paper demonstrates how the framework can be applied to develop an application for monitoring physical activity and heart rate. The framework enables software and service operators to iteratively create, tune and deploy filter algorithms in distributed BLE IoT systems, without rebooting nodes or restarting programs using dynamic software updating.

Today, the programming of a complete wearable sensor system requires writing code in different programming languages for the different parts of the systems, such as the wearable sensor platform itself, the gateway, the back-end server, and the client app. In this paper, we propose to use WebAssembly, which is a simple but powerful virtual machine standard already supported by all major web browsers. We show that it is possible to implement a WebAssembly interpreter for embedded systems, such as the Texas Instruments CC2652R system-on-chip and this enables the same code to execute in all parts of the systems. In our proof-of-concept implementation, we use Bluetooth low energy, which means that smartphones can communicate with and program our device without the need for special hardware.

PerfMon is a prototype implementation of a realtime performance management method for sensor data communication in assisted living applications. It is implemented in accordance with the specification for GATT services in Bluetooth low energy (BLE). PerfMon provides a tool for real-time performance monitoring and control for caregivers and service providers. Test results from monitoring and control of packet loss ratio related to alarm thresholds are presented. PerfMon is adapted to cloud-based web services using RESTful APIs and established object models. Performance management is a necessary component in an overall management system of IoT devices for healthcare and assisted living applications.

Fall injury issues represent a serious problem for elderly in our society. These people want to live in their home as long as possible and technology can improve their security and independence. In this work we study the joint use of a camera based system and wearable devices, in the so called data fusion approach, to design a fall detection solution. The synchronization issues between the heterogeneous data provided by the devices are properly treated, and three different fall detection algorithms are implemented. Experimental results are also provided, to compare the proposed solutions.

This paper evaluates a use-case for smartphone-centric Wi-Fi device-to-device sensor communication that enables user mobility in ambient assisted living (AAL) services. A real-time performance measurement method has been developed and implemented to evaluate the smartphone’s ability to act as a hub and gateway for Wi-Fi connected sensor nodes. The results show that Wi-Fi Direct and Wi-Fi Hotspot are feasible solutions for smartphone-centric device-to-device communication that enables user mobility. In addition, a cloud-based web application for monitoring and displaying sensor data has been implemented.

Ambient Assisted Living applications often need to integrate data from multiple sensors, to provide consistent information on the observed phenomena. Data fusion based on samples from several sensors requires accurate time synchronization with sufficient resolution, depending on the sensor sampling frequency. This work presents a technical platform for the efficient and accurate synchronization of the data captured from RGB-Depth cameras and wearable inertial sensors, that can be integrated in AAL solutions. A case study of sensor data fusion for Timed Up and Go test is also presented and discussed.