Eating Event Recognition Using Accelerometer, Gyroscope, Piezoelectric, and Lung Volume Sensors

In overcoming the worldwide problem of overweight and obesity, automatic dietary monitoring (ADM) is introduced as support in dieting practises. ADM aims to automatically, continuously, and objectively measure dimensions of food intake in a free-living environment. This could simplify the food registration process, thereby overcoming frequent memory, underestimation, and overestimation problems. In this study, an eating event detection sensor system was developed comprising a smartwatch worn on the wrist containing an accelerometer and gyroscope for eating gesture detection, a piezoelectric sensor worn on the jaw for chewing detection, and a respiratory inductance plethysmographic sensor consisting of two belts worn around the chest and abdomen for food swallowing detection. These sensors were combined to determine to what extent a combination of sensors focusing on different steps of the dietary cycle can improve eating event classification results. Six subjects participated in an experiment in a controlled setting consisting of both eating and non-eating events. Features were computed for each sensing measure to train a support vector machine model. This resulted in F1-scores of 0.82 for eating gestures, 0.94 for chewing food, and 0.58 for swallowing food.

Automated Diet Capture Using Voice Alerts and Speech Recognition on Smartphones: Pilot Usability and Acceptability Study

BACKGROUND

Effective monitoring of dietary habits is critical for promoting healthy lifestyles and preventing or delaying the onset and progression of diet-related diseases, such as type 2 diabetes. Recent advances in speech recognition technologies and natural language processing present new possibilities for automated diet capture; however, further exploration is necessary to assess the usability and acceptability of such technologies for diet logging.

OBJECTIVE

This study explores the usability and acceptability of speech recognition technologies and natural language processing for automated diet logging.

METHODS

We designed and developed base2Diet-an iOS smartphone application that prompts users to log their food intake using voice or text. To compare the effectiveness of the 2 diet logging modes, we conducted a 28-day pilot study with 2 arms and 2 phases. A total of 18 participants were included in the study, with 9 participants in each arm (text: n=9, voice: n=9). During phase I of the study, all 18 participants received reminders for breakfast, lunch, and dinner at preselected times. At the beginning of phase II, all participants were given the option to choose 3 times during the day to receive 3 times daily reminders to log their food intake for the remainder of the phase, with the ability to modify the selected times at any point before the end of the study.

RESULTS

The total number of distinct diet logging events per participant was 1.7 times higher in the voice arm than in the text arm (P=.03, unpaired t test). Similarly, the total number of active days per participant was 1.5 times higher in the voice arm than in the text arm (P=.04, unpaired t test). Furthermore, the text arm had a higher attrition rate than the voice arm, with only 1 participant dropping out of the study in the voice arm, while 5 participants dropped out in the text arm.

CONCLUSIONS

The results of this pilot study demonstrate the potential of voice technologies in automated diet capturing using smartphones. Our findings suggest that voice-based diet logging is more effective and better received by users compared to traditional text-based methods, underscoring the need for further research in this area. These insights carry significant implications for the development of more effective and accessible tools for monitoring dietary habits and promoting healthy lifestyle choices.

DynDSE: Automated Multi-Objective Design Space Exploration for Context-Adaptive Wearable IoT Edge Devices

We describe a simulation-based Design Space Exploration procedure (DynDSE) for wearable IoT edge devices that retrieve events from streaming sensor data using context-adaptive pattern recognition algorithms. We provide a formal characterisation of the design space, given a set of system functionalities, components and their parameters. An iterative search evaluates configurations according to a set of requirements in simulations with actual sensor data. The inherent trade-offs embedded in conflicting metrics are explored to find an optimal configuration given the application-specific conditions. Our metrics include retrieval performance, execution time, energy consumption, memory demand, and communication latency. We report a case study for the design of electromyographic-monitoring eyeglasses with applications in automatic dietary monitoring. The design space included two spotting algorithms, and two sampling algorithms, intended for real-time execution on three microcontrollers. DynDSE yielded configurations that balance retrieval performance and resource consumption with an F1 score above 80% at an energy consumption that was 70% below the default, non-optimised configuration. We expect that the DynDSE approach can be applied to find suitable wearable IoT system designs in a variety of sensor-based applications.

EarBit: Using Wearable Sensors to Detect Eating Episodes in Unconstrained Environments

Chronic and widespread diseases such as obesity, diabetes, and hypercholesterolemia require patients to monitor their food intake, and food journaling is currently the most common method for doing so. However, food journaling is subject to self-bias and recall errors, and is poorly adhered to by patients. In this paper, we propose an alternative by introducing EarBit, a wearable system that detects eating moments. We evaluate the performance of inertial, optical, and acoustic sensing modalities and focus on inertial sensing, by virtue of its recognition and usability performance. Using data collected in a simulated home setting with minimum restrictions on participants' behavior, we build our models and evaluate them with an unconstrained outside-the-lab study. For both studies, we obtained video footage as ground truth for participants activities. Using leave-one-user-out validation, EarBit recognized all the eating episodes in the semi-controlled lab study, and achieved an accuracy of 90.1% and an F₁-score of 90.9% in detecting chewing instances. In the unconstrained, outside-the-lab evaluation, EarBit obtained an accuracy of 93% and an F₁-score of 80.1% in detecting chewing instances. It also accurately recognized all but one recorded eating episodes. These episodes ranged from a 2 minute snack to a 30 minute meal.