The Densor is an intraoral sensor platform created to capture unique data from inside the human mouth. This thesis studies the possibility of using Densor-recorded sensor data for Voice Activity Detection (VAD) and basic non-acoustic speech (keyword) recognition. It is part of a broader effort to explore the different use cases of the Densor. This thesis summarizes the performance of existing non-acoustic wearable speech recognition devices, describes the available Densor data, details feature extraction and selection, and shows how Hidden Markov Models can beusedfor both VADandkeyword recognition. The results are promising, with an F1-Score of up to 0.73 for VAD and up to 75% accuracy for keyword recognition. While these results show that both tasks are possible, they cannot be generalized due to the small and unvaried dataset. Future work suggestions include expanding and increasing the variety of the dataset and exploring alternative models such as Conditional Random Fields and Recurrent Neural Networks.

Bruxism is a medical disorder that causes individuals to grind or clench their teeth together. This can cause dental damage, headaches, and jaw disorders. Reliable detection of bruxism remains challenging due to the limitations of current diagnostic methods. This study investigates the potential of using an intraoral device equipped with an accelerometer to detect bruxism events. Machine learning techniques, specifically Hidden Markov Models, were applied to classify accelerometer data collected during controlled experiments. The results indicate that while the system effectively distinguishes between bruxism and non-bruxism activities in general, detecting actual grinding and clenching events remains difficult due to class imbalance and overlapping motion patterns. Nonetheless, the findings suggest that in-mouth accelerometers hold promise for future bruxism detection systems, requiring further data collection and model refinement.

Continuous and prolonged breathing rate monitoring is a challenge. Existing technologies have limitations such as bad user comfort over time or interference from the experimental environment. Intraoral sensors offer a novel approach, measuring at a position directly connected to the lungs and breathing cycles. The objective is to develop and evaluate a breathing rate estimation pipeline using pressure and acceleration sensor measurements.The Densor (dental sensor) is an intraoral sensing device that generated the required data.Participants performed controlled experiments with varying breathing rates and body positions.Pressure and acceleration measurements were filtered using signal processing techniques to isolate human breathing frequencies. An estimate for the breathing rate is subsequently calculated using peak detection algorithms applied to the filtered signals. The model demonstrated a proof of concept, reaching mean absolute percentage errors as low as 5.42% for breathing rate estimates. However, further analysis is needed to establish this method as a reliable estimator of breathing rate.

In the domain of intraoral hydration sensing to monitor oral health, there is sparse research on continuous capacitive sensing. Most devices can only perform a single point of measurement, making it unsuitable for wearing long-term. This paper aims to research the possibility of expanding the Densor developed by Dsouza et al. [4], with a capacitive sensor to measure hydration in the mouth, by testing it on NaCl-demineralised water solutions. The obtained data does not yet show any strong correlation between salt values and measured values, suggesting the sensor might not be suitable for this purpose, but the data is yet insufficient to draw any definite conclusions.

This research investigates the detection of gestures using a torso-worn accelerometer sensor. Using the Conflab dataset, we focus on gestures during conversations in mingling scenarios. Due to significant variability in gesture styles among individuals, traditional methods face challenges in building personalized models. Our experiments demonstrate that Transductive Parameter Transfer (TPT), an adaptive transfer learning method, can more effectively model these individual differences in gesturing. To gain insights into individual expressiveness, we classify gestures into three classes: 'no gesture,' 'normal,' and 'large' gestures. TPT performed an average AUC score of 0.84 in binary classification and 0.77 in multiclass classification. These findings highlight the potential of using a single torso-worn accelerometer to understand social behavior in naturalistic settings.

Human activity recognition plays an interesting and important role nowadays as there are a variety of use cases. It is utilized in health monitoring, in the development of human-computer interaction system and in security monitoring. However current methods involve usage of privacy sensitive data and impractical sensors for everyday usage. To tackle this problem, we aim to answer the research question "How to maximize the capabilities of in-mouth sensors for human activity recognition?". The main contributions of this paper are the classification of different gestures using an in-mouth device, implementation of a classifier directly onto a microcontroller and the evaluation whether the models can generalize to multiple people. To investigate this, we experimented with popular classical machine learning classifiers: Decision Tree, K-Nearest Neighbors, Support Vector Machine, Logistic Regression and Random Forest classifiers. The results shows that the F1-score of all classification problems are above 80% using the various classifiers along with different parameters.

Multiactivity analysis investigates one's coordination of actions within a social context, such as gestures and speech, usually using video recordings of the social activity, to further understand the rules of human behaviour. This paper focuses specifically on the coordination between speaking and drinking activities within a social setting, and explores the possibility of automatically identifying these events using audio captured from a drinking glass. As social interactions occur in vastly different contexts, this paper also investigates the effect that background noise might have on the accuracy of identifying these events. Different parameters and audio features were compared. Linear classification models LR and SVM with a linear kernel were able to achieve 100% accuracy for all sample lengths between 2 and 8 seconds using the first 20 PCA components from 60 audio features. The best performing feature in identifying speaking and drinking events was MFCCs, achieving an F₁ score of 99.4% on average across models with a training sample length of 3 seconds. Background noise had different effects on classification accuracy depending on the type, with music lowering the F₁ score to 74.3%, noisy room audio to 64.7%, and podcast audio simulating the presence of other speakers to 59.6% using MFCCs and a 3-second sample length

This research explores the correlation between chewing activities and non-chewing activities using an Arduino microcontroller. Chewing samples are recorded by attaching the microcontroller to the back of the jaw, underneath the ear. The microcontroller collects data from a microphone capturing audio data of chewing sounds, as well as an Inertial Measurement Unit (IMU) collecting motion and orientation data of jaw movements.

The collected data is processed using signal processing techniques, extracting relevant features from the microphone data, such as intensity, frequency content, and features related to acceleration, orientation, and jaw movement patterns from the IMU data. Statistical analysis, employing correlation metrics like Pearson correlation coefficient and Spearman's rank correlation coefficient, determines the correlation between the extracted features from the microphone and IMU data.

Conclusions from the analysis indicate that pre-processing and feature extraction techniques are needed to establish meaningful correlations between the IMU and microphone data. The sliding window approach shows promising results, particularly in correlating the sum of energy from the audio with the sum of gyro data, specifically in the y- and z-axes. The accelerometer data does not exhibit significant correlations, but it can be useful as a threshold for detecting the start of chewing events based on zero crossings.

Furthermore, the findings reveal that food texture and density play a larger role than anticipated in determining the correlation between chewing patterns and sensory data. The research outcomes contribute to various fields, including dentistry, nutrition, and human-computer interaction.

Analyzing food consumption patterns can provide valuable insights into the development of obesity and eating disorders. The detection and quantification of chewing strokes are essential to facilitate this analysis. One approach to food intake analysis involves evaluating chewing sounds generated during the eating process. These sounds were recorded by microphones placed to the user’s outer
ear canal. Aside from discovering the most accurate solution, the algorithms used must demonstrate sufficient efficiency to operate on low-power embedded ear-worn hardware. Three algorithms for automated chewing detection were evaluated with the help of two datasets. The first dataset consists of the food intake sounds from the consumption of three types of food. The second dataset consists of environmental noise. The data were manually labeled by recognizing mastication sounds’ visual and audio characteristics. Precision of over 80%
was achieved by all algorithms in the dataset consisting of only chewing sounds. Finally, an efficient solution has been developed to distinguish between speech and chewing sounds.