15 / 2021 – 06 / 2022
Modern hearing aids are more than just amplifying devices. Most of them are already equipped with inertial sensors. The data of these sensors opens the option to use those devices for Human Activity Recognition (HAR). While today HAR is mostly performed based on sensors on the wrist or waist, the head has also proven to be a viable location that provides good results [1, 2]. The recognition of certain activities could then be used to adapt the hearing aid amplification settings based on the current activity or to monitor the user’s health. As most people who wear hearing aids are elderly, this can include early diagnosis of diseases or the observation of rehabilitation  and health treatments, which require patient movement such as diabetes or heart diseases . A great advantage of performing HAR in hearing aids is that it would be entirely unobtrusive for the users because they would not need any additional device that they might forget to wear.
HAR is a very active field of research in which deep learning (DL) models have shown superior results compared to traditional machine learning models as they do not require experts to extract manual features and improved recognition rates on temporal features . However, those models are often computational complex, making integration into mobile and embedded devices an open challenge .
The main goal of this thesis is to create a DL model for HAR based on the hearing aid’s Inertial Measurement Unit (IMU) sensor data. The model should achieve reasonable recognition rates on the one hand and be light weighted and efficient enough to run on the hearing aid on the other. In addition, we want to implement a traditional feature-based machine learning model and compare that in model size and recognition rate with our DL model.
Therefore, the first step will be to design and implement at least two DL models and a featurebased machine learning model. This will include the fundamental steps of the activity recognition chain, which are data acquisition, segmentation, feature calculation, modeling and inference, and classification . The data for our models have already been recorded by another study and contains accelerometer and gyroscope data. As gyroscopes are not as commonly used in hearing aid as accelerometers and need a significant amount of additional energy, we also want to evaluate the performance differences with and without the gyroscope data. Other works have shown different results on performing HAR with and without gyroscope data. For instance, Wolff et al.  and Haescher et al.  only show a slight difference in the recognition performance, while Ordóñez et al.  showed that there was a noticeable difference. Whereas the first two also used the head as a sensor location, Ordóñez et al.  had a more complex setup with different body sensors.
In the second step, we want to implement and evaluate different compression techniques on the DL models regarding the trade-off in model performance and model compression. That can include methods like pruning, quantization , or a possible sparsification of the model, as shown by Bhattacharya et al. . They demonstrated that a Convolutional Neural Network (CNN) could run on a mobile processor such as the Qualcomm Snapdragon 400.We will compare the compressed models to our feature-based machine learning models regarding model size, inference time, and recognition performance.
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