Towards an Ambient Estimation of Stool Types to Support Nutrition Counseling for People affected by the Geriatric Frailty Syndrome

Elfert, Patrick; Siggelkow, Simon; Eichelberg, Marco; Hein, Andreas

doi:10.1109/iscc50000.2020.9219713

Cited by 5 publications

(5 citation statements)

References 17 publications

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“…Accordingly, active self-managed documentation can become a burden. For this reason, research is currently being conducted on an automatic and ambient classification of stool forms to extend the system described in this study [ 63 ].…”

Section: Discussionmentioning

confidence: 99%

A Novel Digital Nutrition Diary for Geriatric Patients at High Risk of Frailty Syndrome

et al. 2022

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Due to the physical, psychological, or socioeconomic changes that accompany aging, many people will be affected by geriatric frailty syndrome, which can lead to multimorbidity and premature death. Nutrition counseling is often used to prevent and intervene in frailty syndrome, especially in geriatric rehabilitation. To this end, the consumption behavior of geriatric patients is recorded using paper-based, as well as retrospective memory logs in face-to-face interviews between patients and nutritionists. To simplify this procedure, a digital nutrition diary was developed that is specially adapted to the needs of geriatric patients (>=70 years), enabling them to record their consumption behavior themselves. In an initial study (Study 1), conducted in a geriatric rehabilitation division with twelve subjects (ten male, two female, mean age 79.2 ±5.9 years), feedback about the usability of the digital nutrition diary, and how to improve it, was surveyed. In addition, the usability of an activity tracker and a body composition scale was surveyed to determine whether geriatric patients are generally able to use these devices. In a second study (Study 2), also conducted in the geriatric rehabilitation division, this time with sixteen subjects (ten male, six female, mean age 79.3 ±3.9 years), the usability of the digital nutrition diary was surveyed again to evaluate its modifications based on the feedback from Study 1. In Study 1, the usability rating of the system (0–100) was 82.5 for the activity tracker, 29.71 for the body composition scale, and 51.66 initially for the digital nutrition diary, which increased to 76.41 in Study 2.

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Section: Discussionmentioning

confidence: 99%

A Novel Digital Nutrition Diary for Geriatric Patients at High Risk of Frailty Syndrome

et al. 2022

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“…To operationalize our second research question and to help the participants to gain a better understanding of different monitoring solutions, we developed three scenarios for monitoring common major side effects of immunotherapy: (1) insomnia and coughing [ 25 , 26 ], (2) fatigue and dyspnea [ 33 , 34 ], and (3) diarrhea [ 35 ] (see Figure 1 ) with the support of our colleagues from the division for assistance systems and medical technology (A.H.).…”

Section: Methodsmentioning

confidence: 99%

Needs of Lung Cancer Patients Receiving Immunotherapy and Acceptance of Digital and Sensor-Based Scenarios for Monitoring Symptoms at Home—A Qualitative-Explorative Study

Kutzleben

Galuska

Hein

et al. 2022

IJERPH

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Background: The development of immunotherapy in the treatment for lung cancer has changed the outlook for both patients and health care practitioners. However, reporting and management of side effects are crucial to ensure effectiveness and safety of treatment. The aim of this study was to learn about the subjective experiences of patients with lung cancer receiving immunotherapy and to explore their potential acceptance of digital and sensor-based systems for monitoring treatment-related symptoms at home. Methods: A qualitative-explorative interview study with patients with lung cancer (n = 21) applying qualitative content analysis. Results: Participants had trouble to classify and differentiate between symptoms they experienced and it seemed challenging to assess whether symptoms are serious enough to be reported and to figure out the right time to report symptoms to health care practitioners. We identified four basic needs: (1) the need to be informed, (2) the need for a trustful relationship, (3) the need to be taken seriously, and (4) the need for needs-oriented treatment concepts. The idea of digital and sensor-based monitoring initially provoked rejection, but participants expressed more differentiated attitudes during the interviews, which could be integrated into a preliminary model to explain the acceptance of digital and sensor-based monitoring scenarios. Conclusions: Supporting lung cancer patients and their health care providers in communicating about treatment-related symptoms is important. Technology-based monitoring systems are considered to be potentially beneficial. However, in view of the many unfulfilled information needs and the unsatisfactory reporting of symptoms, it must be critically questioned what these systems can and should compensate for, and where the limits of such monitoring lie.

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“…Therefore, we separately trained and tested a SegNet model using the same as the inference time, F-score, and mIoU. We also compared our F-score and mIoU ratings to Otsu's threshold-based edge detection method [49], which had previously been used by researchers in the same field [27], [28], [30].…”

Section: ) Comparison With State-of-the-art Methodsmentioning

confidence: 99%

“…Nevertheless, object detection of human stools has been strategically challenging due to a lack of annotated stool image datasets, which have prevented the widespread use of the automatic stool detection and tracking system. Some methods used threshold-based segmentation techniques to localize the stool region in the original images [27], [28], [30]. However, because of the inability to remove all unrelated background information, these methods frequently produced unsatisfactory predictions.…”

Section: Related Work a Medical Image Segmentationmentioning

confidence: 99%

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Detection and Classification of Human Stool Using Deep Convolutional Neural Networks

Choy¹,

Chen

2021

IEEE Access

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The diagnosis of functional gastrointestinal disorders and chronic digestive system diseases such as irritable bowel syndrome relies heavily on macroscopic examination of human stool specimens. However, traditional manual stool analysis processes are time-consuming and prone to human subjectivity errors that may lead to incorrect judgments. In this study, we employed deep convolutional neural networks (CNN) to automatically recognize and classify stools in macroscopic images. This approach is advantageous because it reduces the amount of direct interaction required by patients or medical staff, lowers the risk of cross-infection, and removes subjectivity in stool analysis. The U-Net segmentation model is applied to define the region of stools by generating a mask image. After combining the mask and the corresponding original image, the ResNeXt-50 classifies the pre-processed stool image according to the modified Bristol Stool Form Scale (BSFS). Overall, the U-Net model yielded a mean intersection-over-union (mIoU) of 93.75% and an F-score of 0.9570, as the ResNeXt-50 classifier had a classification accuracy of 94.35% and showed decent performance in terms of predictive power. Our study may help to improve the quality of diagnosis and monitoring of diseases associated with bowel movement habits by providing reliable measurements in terms of stool form and consistency.

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Towards an Ambient Estimation of Stool Types to Support Nutrition Counseling for People affected by the Geriatric Frailty Syndrome

Cited by 5 publications

References 17 publications

A Novel Digital Nutrition Diary for Geriatric Patients at High Risk of Frailty Syndrome

A Novel Digital Nutrition Diary for Geriatric Patients at High Risk of Frailty Syndrome

Needs of Lung Cancer Patients Receiving Immunotherapy and Acceptance of Digital and Sensor-Based Scenarios for Monitoring Symptoms at Home—A Qualitative-Explorative Study

Detection and Classification of Human Stool Using Deep Convolutional Neural Networks

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