A wise neckband permits wearers to watch their dietary consumption. Robotically monitoring meals and fluid consumption will be helpful when managing circumstances together with diabetes and weight problems, or when maximizing health. However wearable applied sciences should be capable of distinguish consuming and ingesting from comparable actions, reminiscent of talking and strolling. Chi Hwan Lee and colleagues suggest a machine-learning enabled neckband that may differentiate physique actions, speech, and fluid and meals consumption. The neckband’s sensor module features a floor electromyography sensor, a three-axis accelerometer, and a microphone. Collectively, these sensors can seize muscle activation patterns within the thyrohyoid muscle of the neck, together with physique actions and acoustic indicators. In a research of six volunteers, the machine-learning algorithm appropriately decided which actions have been consuming or ingesting with an accuracy price of about 96% for particular person actions and 89% for concurrent actions. The neckband is manufactured from a stretchable, twistable, breathable, mesh-structured textile loaded with 47 lively and passive parts that may run on battery energy for greater than 18 hours between prices. In keeping with the authors, the neckband could possibly be utilized in a closed-loop system mixed with steady glucose meter and insulin pump to calculate insulin dosages for diabetic sufferers by figuring out meal timings—or to assist athletes and different people fascinated about growing their total well being and wellness.
Credit score: Park et al
A wise neckband permits wearers to watch their dietary consumption. Robotically monitoring meals and fluid consumption will be helpful when managing circumstances together with diabetes and weight problems, or when maximizing health. However wearable applied sciences should be capable of distinguish consuming and ingesting from comparable actions, reminiscent of talking and strolling. Chi Hwan Lee and colleagues suggest a machine-learning enabled neckband that may differentiate physique actions, speech, and fluid and meals consumption. The neckband’s sensor module features a floor electromyography sensor, a three-axis accelerometer, and a microphone. Collectively, these sensors can seize muscle activation patterns within the thyrohyoid muscle of the neck, together with physique actions and acoustic indicators. In a research of six volunteers, the machine-learning algorithm appropriately decided which actions have been consuming or ingesting with an accuracy price of about 96% for particular person actions and 89% for concurrent actions. The neckband is manufactured from a stretchable, twistable, breathable, mesh-structured textile loaded with 47 lively and passive parts that may run on battery energy for greater than 18 hours between prices. In keeping with the authors, the neckband could possibly be utilized in a closed-loop system mixed with steady glucose meter and insulin pump to calculate insulin dosages for diabetic sufferers by figuring out meal timings—or to assist athletes and different people fascinated about growing their total well being and wellness.
Article Title
A machine-learning-enabled good neckband for monitoring dietary consumption
Article Publication Date
7-Might-2024
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