Fluid intake recognition using inertial sensors

Wellnitz, Arne; Wolff, Johann-Peter; Haubelt, Christian; Kirste, Thomas

doi:10.1145/3361684.3361688

Cited by 9 publications

(10 citation statements)

References 13 publications

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“…Several wearable techniques are proposed to detect food or liquid intake including inertial measurements of the wrist or body [ 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 ], or textile-based measurements [ 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 ]. The majority of studies analyzed and classified food intake with less focus on liquid intake.…”

Section: Wearable Technologymentioning

confidence: 99%

“…Several research papers have used Inertial Measurement Units (IMUs) on the wrist to measure liquid intake. Most previous work focused on intake recognition, but did not attempt to provide an intake volume estimate [ 45 , 46 , 47 , 48 , 49 ]. The wrist-mounted inertial sensors are often used in combination with machine learning (ML) algorithms to detect drinking events.…”

Section: Wearable Technologymentioning

confidence: 99%

“…The majority of recent papers used Artificial Intelligence (AI) to detect drinking using wearable devices. Considering single wrist inertial devices, fluid intake classification was performed using Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) networks or LSTM alone [ 46 , 52 ], hierarchical temporal memory (HTM) [ 48 ], log regression with Artificial Neural Networks (ANN) [ 53 , 54 ], Random Forest (RF) [ 47 , 54 ], Hidden Markov Methods (HMMs) with k-nearest neighbors (kNN) [ 55 ] and HMMs with Gaussian Mixture Modelling (GMM) [ 56 ]. Weiss et al tested several ML algorithms on various Activities of Daily Living (ADL), including but not specific to drinking actions.…”

Section: Wearable Technologymentioning

confidence: 99%

Section: Wearable Technologymentioning

confidence: 99%

“…From the studies including a Null class, Wellnitz et al achieved an average accuracy of 95.7% for drinking detection using a single IMU on the wrist and 81.4% when placing the IMU on eye glasses [ 46 ]. Moreover, classical algorithms including Support Vector Machine (SVM), kNN and RF performed as well as the hybrid CNN-LSTM deep learning model [ 46 ]. This study also found that there was a low correlation (0.49) between length of drink and volume consumed across the 41 participants, showing that this is not a reliable way to estimate volume intake [ 46 ].…”

Section: Wearable Technologymentioning

confidence: 99%

See 4 more Smart Citations

Fluid Intake Monitoring Systems for the Elderly: A Review of the Literature

2021

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Fluid intake monitoring is an essential component in preventing dehydration and overhydration, especially for the senior population. Numerous critical health problems are associated with poor or excessive drinking such as swelling of the brain and heart failure. Real-time systems for monitoring fluid intake will not only measure the exact amount consumed by the users, but could also motivate people to maintain a healthy lifestyle by providing feedback to encourage them to hydrate regularly throughout the day. This paper reviews the most recent solutions to automatic fluid intake monitoring both commercially and in the literature. The available technologies are divided into four categories: wearables, surfaces with embedded sensors, vision- and environmental-based solutions, and smart containers. A detailed performance evaluation was carried out considering detection accuracy, usability and availability. It was observed that the most promising results came from studies that used data fusion from multiple technologies, compared to using an individual technology. The areas that need further research and the challenges for each category are discussed in detail.

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Section: Wearable Technologymentioning

confidence: 99%

Section: Wearable Technologymentioning

confidence: 99%

Section: Wearable Technologymentioning

confidence: 99%

Section: Wearable Technologymentioning

confidence: 99%

Section: Wearable Technologymentioning

confidence: 99%

See 3 more Smart Citations

Fluid Intake Monitoring Systems for the Elderly: A Review of the Literature

2021

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Practicality Aspects of Automatic Fluid Intake Monitoring via Smartwatches

Lutze¹,

Waldhör

2023

Lecture Notes in Computer Science

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Drinking Gesture Detection Using Wrist-Worn IMU Sensors with Multi-Stage Temporal Convolutional Network in Free-Living Environments

Wang

Kumar

Raedt

et al. 2022

2022 44th Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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Maintaining adequate hydration is important for health. Inadequate liquid intake can cause dehydration problems. Despite the increasing development of liquid intake monitoring, there are still open challenges in drinking detection under free-living conditions. This paper proposes an automatic liquid intake monitoring system comprised of wrist-worn Inertial Measurement Units (IMUs) to recognize drinking gesture in free-living environments. We build an end-to-end approach for drinking gesture detection by employing a novel multi-stage temporal convolutional network (MS-TCN). Two datasets are collected in this research, one contains 8.9 hours data from 13 participants in semi-controlled environments, the other one contains 45.2 hours data from 7 participants in free-living environments. The Leave-One-Subject-Out (LOSO) evaluation shows that this method achieves a segmental F1-score of 0.943 and 0.900 in the semi-controlled and free-living datasets, respectively. The results also indicate that our approach outperforms the convolutional neural network and long-short-term-memory network combined model (CNN-LSTM) on our datasets. The dataset used in this paper is available at https://github.com/Pituohai/drinkinggesture-dataset/.Clinical relevance-This automatic liquid intake monitoring system can detect drinking gesture in daily life. It has the potential to be used to record the frequency of drinking water for at-risk elderly or patients in the hospital.

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Fluid intake recognition using inertial sensors

Cited by 9 publications

References 13 publications

Fluid Intake Monitoring Systems for the Elderly: A Review of the Literature

Fluid Intake Monitoring Systems for the Elderly: A Review of the Literature

Practicality Aspects of Automatic Fluid Intake Monitoring via Smartwatches

Drinking Gesture Detection Using Wrist-Worn IMU Sensors with Multi-Stage Temporal Convolutional Network in Free-Living Environments

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