With the rise of wrist-worn devices like smartwatches and fitness trackers and the integration of Inertial Measurement Unit (IMU) sensors questions about the privacy impact of their recorded data arise which often gets little attention in privacy considerations. Worn on the wrist one possible impact is a possible eavesdropper inferring the handwriting done by the wearer of the device using the collected IMU data. Another use case is the deliberate digitizing of handwriting by users wearing such devices. In this case it is also feasible for the user to wear an additional device to improve the digitizing.

In this thesis we investigate both the possible privacy impact and the possibilities for a deliberate digitizing of handwriting done on paper based on IMU sensor data recorded on a smartwatch. Furthermore, we collect Electromyography (EMG) sensor data using an armlet worn on the lower arm to analyze if the original recognition results can be improved utilizing these data. We design and conduct a data study aimed at mirroring everyday circumstances using an Apple Watch and a Thalmic Myo armlet to record the necessary data. Additionally, the original handwriting of the study participants is digitized by writing on paper on top of a Wacom Bamboo Slate tablet. We use the recorded continuous streams of IMU and EMG data to classify the written letters using the 1-Nearest Neighbor (1NN) algorithm in combination with the Dynamic Time Warping (DTW) algorithm. Our model achieves widely varying results depending on the writer and an overall accuracy of 0.28. Very low accuracies for the classification based on EMG data prevent us from evaluating possible improvements when combining both data types. Our findings suggest that the recognition depends on the writing style of the individual user and more research is required to make the handwriting recognition based on IMU or EMG data applicable to writing in everyday life.