In the last 4 decades, surface electromyography (sEMG) signal processing has been applied to detect local muscle fatigue, this non-invasive approach is suitable for detecting EMG signals generated by athletes in motion. Also, EMG could directly reveal the muscle’s performance like endurance and recruitment of motor units, which is hard to be obtained by other methods. With the sEMG system, we can research whether EMG signals can be used to measure muscle fatigue and how this relates to injury risk. This thesis aims to build a sensor node for sEMG to detect local muscle fatigue. An sEMG system is built for this purpose, and a physiological experiment is designed to collect sEMG signals from human muscle (Vastus Medialis) using the sEMG system. Both isometric and isotonic exercises are studied. The data analyzing method is calculating mean power spectrum frequency (MNF), median power spectrum frequency (MDF), and muscle fiber propagation velocity (MFPV) of the collected sEMG signals, because local muscle fatigue is related to MNF/MDF decrease and MFPV decrease. 5 groups of isometric exercise, wall-sit and 2 groups of isotonic exercise, cycling, are recorded. All the athletes are healthy males, around 25. The data analyzing result shows that MNF/MDF decrease is related to muscle fatigue, and MFPV changes similarly with MNF/MDF.

In football, a lot of hip and thigh muscle injuries occur as a result of high muscular loads due to accelerative leg movements. To prevent muscle damage and optimise performance, it is essential to continuously identify when and how frequent local hip and thigh muscular loads develop in the explosive and dynamic football environment. The currently used method is an acceleration index based on two-dimensional position data of the whole global body measured by the Local Positioning Measurement system. The problem is that this system does not correspond with the experienced load of players because leg movements are excluded. Therefore, this study introduces a new local concept of gathering local three-dimensional leg acceleration data by inertial measurement units.
This pilot study aims to use a big data analysis approach to translate leg acceleration data into a measure to indicate local muscle load and compare this new local and the current global method to the players’ experienced load. Five participants performed specific football drills with an intensity increase from jogging to sprinting and by adding a pass and shot. Measures are developed, based on the pelvis, upper leg, and lower leg accelerations, by a peak and cumulative data analysis approach. By evaluating trend percentages of the intensity increase, it is obtained that a local acceleration measure is comparable to the players’ experienced load if it considers the sum of normal or peak data points weighted per zone and per travelled distance. Furthermore, a similar result is obtained when only the upper leg or lower leg accelerations are considered.
It can be concluded that local three-dimensional acceleration of the lower extremities, processed with a big data analysis approach, represent the football players’ experienced muscular load more accurate than the current global method. Further research, including a higher number of participants, should prove the significance.

Multiple studies indicated that the degree of muscle strain is the most relevant parameter in understanding the injury mechanism behind a hamstring strain injury. To monitor this parameter a new system is developed; the Smart Sensor Shorts. The system contains five Inertial Measurement Units (IMUs) integrated in a sports tights. The purpose of this study was to develop a methodology to estimate the muscle strain and muscle elongation velocity of the biceps femoris (BF), semimembranosus (SM) and the semitendinosus (ST) muscle in professional and recreational football athletes with the use of IMUs during different football specific movements and during different intensities. When comparing different movements and intensities, the greatest peak muscle strain was found in the BF and the lowest peak muscle strain was found in the SM during the majority of the movements. The biomechanical load was different for each hamstring muscle and was different for the running based movements and other football specific movements. The BF experienced the greatest peak muscle strain (12.12 ± 0.88%) during a maximal intensity kick in the supporting leg, while the greatest peak muscle elongation velocity was observed in the ST (3.97 ± 0.47 s-1) in the kicking leg during the maximal intensity kick. The greatest biomechanical loading was during a maximal intensity kick. Finally, it was observed that the moment of peak muscle strain was different from the time period of peak muscle elongation velocity for running based movements. It is concluded that IMUs together with the developed methodology could be used in the assessment of hamstring strain injuries in professional and recreational football by analyzing and monitoring muscle strain and muscle elongation velocity.

This research provide a proof of principle to use a sweat sensor system for real-time monitoring of medicine effectiveness in Cystic Fibrosis (CF) patients. CF is an autosomal recessive genetic disorder affecting mostly the respiratory, digestive and perspiration system. Patients with CF have dysfunctional chloride channels in their cells, due to mutations in both copies of the gene for the CFTR protein. The CFTR proteins are necessary for the production of mucus, a malfunction of the CFTR protein will result in tough mucus. In CF, a lack of functional CFTR prevents normal sodium and chloride absorption in sweat and leads to excessive salt loss. Due to increased sodium and chloride concentration in sweat from CF patients, sweat makes a good clinical body fluid to indicate the medicines' effectiveness. By measuring the chloride or sodium concentration before treatment with the medicine and after treatment, an indication of the medicines’ effectiveness can be obtained. In this study, a potentiometric screen-printed sweat sensor has been developed to monitor the medicine effectiveness in CF patients. The sensor consists of a reference electrode and ion-selective electrodes for measurements of chloride and sodium concentrations. Multiple prototypes of the sensor have been developed and evaluated on their performance. Furthermore, a read-out circuit with low leakage/bias currents and 8 channels is designed to increase the read-out accuracy and speed. Since the sweat volume during rest appeared to be too low for real-time measurements, a sweat collector was implemented with the sensor to increase the sweat sample volume during real-time measurements. Furthermore, a pilocarpine sweat stimulator has been designed and tested to artificially increase the sweat rate.
The developed system proofed to be a functional concept for real-time patient monitoring. In future research, the chemical structure of the membranes is the most important topic to be improved. Improvements in this field could extend the life-time of the sensor and would minimise the sensitivity differences between the sensors. Finally, the sweat sensor, collector and stimulator have to be integrated and minimised in one design to make a wearable device out of it.