Wearable devices are a popular training tool to measure biomechanical performance indicators during running, including vertical oscillation (VO). VO is a contributing factor in running economy and injury risk, therefore VO feedback can have a positive impact on running performance. The validity and reliability of the VO measurements from wearable devices is crucial for them to be an effective training tool. The aims of this study were to test the validity and reliability of VO measurements from wearable devices against video analysis of a single trunk marker. Four wearable devices were compared: the INCUS NOVA, Garmin Heart Rate Monitor-Pro (HRM), Garmin Running Dynamics Pod (RDP), and Stryd Running Power Meter Footpod (Footpod). Fifteen participants completed treadmill running at five different self-selected speeds for one minute at each speed. Each speed interval was completed twice. VO was recorded simultaneously by video and the wearables devices. There was significant effect of measurement method on VO (p < 0.001), with the NOVA and Footpod underestimating VO compared to video analysis, while the HRM and RDP overestimated. Although there were significant differences in the average VO values, all devices were significantly correlated with the video analysis (R > = 0.51, p < 0.001). Significant agreement between repeated VO measurements for all devices, revealed the devices to be reliable (ICC > = 0.948, p < 0.001). There was also significant agreement for VO measurements between each device and the video analysis (ICC > = 0.731, p < = 0.001), therefore validating the devices for VO measurement during running. These results demonstrate that wearable devices are valid and reliable tools to detect changes in VO during running. However, VO measurements varied significantly between the different wearables tested and this should be considered when comparing VO values between devices.

No abstract

Air pollution and increasing traffic congestion means the current way of navigating through a city needs to be rethought. One of the possible solutions is to move away from internal combustion engines and embrace electric and hybrid vehicles. Electric Bicycles can offer an alternative to traditional modes of transport and support an environmentally friendly way to navigate an urban environment, with the benefit of encouraging physical exercise. There are still several issues that constrain a large-scale acceptance of Electric Bicycles, including the need for personalised controller strategies and the energy efficiencies. Current strategies do not include any analysis of rider's capabilities, physiological factors or pedalling techniques. The research outlined in this paper involved 30 participants that volunteered to take part in an Incremental Sub-Maximal Ramp Test with the aim of understanding and quantifying pedalling characteristics and demonstrating that a better motor controller strategy tailored towards individual requirements is possible. Gender and Cycling Experience were the most prominent factors that differentiate the capabilities of the population. Three novel controller techniques (i.e. Fixed Percentage, Torque Filling and Real-Time Power mapping) are analysed and presented as innovative methods for next generation personalised controller strategies for Electric Bicycles.

• This is an Accepted Manuscript of a paper published by CRC Press in INTRODUCTIONModelling manufacturing processes which contain human interactions is difficult and can produce unrealistic views of the process. This is because in many companies the actual manufacturing process that takes place is not as planned when human interaction is involved. Human factors can determine what actually happens, the time it takes and what order it happens in. To produce a more reliable representation of the process more information on what is actually happening is required. This can be found by tracking and recording the process using radiofrequency identification (RFID) tags (Weinstein, 2005). From the data produced from these tags the possible paths which products take in the process can be determined and hence the actual manufacturing process can be defined. Furthermore the data can be used to form Markov chains which can determine what future process routes will look like and the probability of each route. Basing future business simulations on these Markov chains can give a more reliable representation of the business. This reduces the risk of modelling inaccuracies and can help to predict future outcomes and run optimisation more accurately.The research performed here studies a company which refurbishes IT products. The company has a business model of the manufacturing process which it expects the products to follow. The company has tracked their products through the refurbishment process using RFID tags to determine what processes each product undertakes and to allow each object to be kept track of. The RFID tags communicate the process information of each product to a database software using RFID tag readers. The information from these RFID tags is used in the work presented here to form a Markov chain representation of the business process routes.Companies' model manufacturing processes for many reasons, including predicting cost (Rehman et al., 1998), predicting resource and material demand and running optimisation studies.When modelling the data produced from the RFID tags the Markov chain produced gives a large variety of process routes. These are not all true reflections of the routes products take. The data mining process from the RFID tag data is also investigated to allow the development of more precise process models. This process allows thresholds to be set for each route. Hence irregular paths are removed.The Markov chain is necessary to simulate the product flow. The process is simulated using the Markov chain produced from the data and the results can be compared to the process simulated based on previous perceptions of the business process. The results produced will include the time taken for each product to be processed, the cost of the process and the final destination of products. The Markov chain ABSTRACT: Optimizing manufacturing processes with inaccurate models of the process will lead to unreliable results. This can be true when there is a strong human influence on the manufacturing process and man...

No abstract