Assessing the Accuracy of a Wrist Motion Tracking Method for Counting Bites across Demographic and Food Variables
This paper describes a study to test the accuracy of a method that tracks wrist motion during eating to detect and count bites. The purpose was to assess its accuracy across demographic (age, gender, ethnicity) and bite (utensil, container, hand used, food type) variables. Data were collected in a cafeteria under normal eating conditions. A total of 271 participants ate a single meal while wearing a watch-like device to track their wrist motion. Video was simultaneously recorded of each participant and subsequently reviewed to determine the ground truth times of bites. Bite times were operationally defined as the moment when food or beverage was placed into the mouth. Food and beverage choices were not scripted or restricted. Participants were seated in groups of 2-4 and were encouraged to eat naturally. A total of 24,088 bites of 374 different food and beverage items were consumed. Overall the method for automatically detecting bites had a sensitivity of 75% with a positive predictive value of 89%. A range of 62-86% sensitivity was found across demographic variables, with slower eating rates trending towards higher sensitivity. Variations in sensitivity due to food type showed a modest correlation with the total wrist motion during the bite, possibly due to an increase in head-towards-plate motion and decrease in hand-towards-mouth motion for some food types. Overall, the findings provide the largest evidence to date that the method produces a reliable automated measure of intake during unrestricted eating.
Code (0)
등록된 구현이 없습니다.
Tasks
SensitivitySimilar Papers 제목 키워드 기반
Earable and Wrist-worn Setup for Accurate Step Counting Utilizing Body-Area Electrostatic Sensing
Step-counting has been widely implemented in wrist-worn devices and is accepted by end users as a quantitative indicator of everyday exercise. However, existing counting approach (mostly on wrist-worn setup) lacks robust…
The Contribution of Human Body Capacitance/Body-Area Electric Field To Individual and Collaborative Activity Recognition
The current dominated wearable body motion sensor is IMU. This work presented an alternative wearable motion-sensing approach: human body capacitance (HBC, also commonly defined as body-area electric field). While being …
Activity RecognitionWristMimic: Full-Body Humanoid Control with Wrist-Guided Manipulation
Retargeting human object interaction demonstrations to physics based simulation requires reproducing not only body motion but also the object motion and contacts that make manipulation succeed. However, position only han…
Object TrackingHOOV: Hand Out-Of-View Tracking for Proprioceptive Interaction using Inertial Sensing
Current Virtual Reality systems are designed for interaction under visual control. Using built-in cameras, headsets track the user's hands or hand-held controllers while they are inside the field of view. Current systems…
Towards Whole Hand and Wrist Kinematic Tracking with a Wearable A-Mode Ultrasound Probe
A-mode ultrasound (US) has emerged as a promising modality for hand and wrist motion tracking. Prior works have mainly addressed static gesture classification or regression of a few degrees of freedom (DoFs), typically r…