Automated Bowel Sound and Motility Analysis with CNN Using a Smartphone

Kutsumi, Yuka; Kanegawa, Norimasa; Zeida, Mitsuhiro; Matsubara, Hitoshi; Murayama, Norihito

doi:10.3390/s23010407

Cited by 13 publications

(9 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Yuka Kutsumi et al collected bowel sound (BS) data from 100 participants using a smartphone. They developed a CNN model which is capable of classifying BSs with an accuracy of 98% to comment on the gut health of a person [ 6 ]. Renisha Redij et al also illustrated the application of AI in classifying BSs.…”

Section: Related Workmentioning

confidence: 99%

Design and Development of a Non-Contact ECG-Based Human Emotion Recognition System Using SVM and RF Classifiers

2023

View full text Add to dashboard Cite

Emotion recognition becomes an important aspect in the development of human-machine interaction (HMI) systems. Positive emotions impact our lives positively, whereas negative emotions may cause a reduction in productivity. Emotionally intelligent systems such as chatbots and artificially intelligent assistant modules help make our daily life routines effortless. Moreover, a system which is capable of assessing the human emotional state would be very helpful to assess the mental state of a person. Hence, preventive care could be offered before it becomes a mental illness or slides into a state of depression. Researchers have always been curious to find out if a machine could assess human emotions precisely. In this work, a unimodal emotion classifier system in which one of the physiological signals, an electrocardiogram (ECG) signal, has been used is proposed to classify human emotions. The ECG signal was acquired using a capacitive sensor-based non-contact ECG belt system. The machine-learning-based classifiers developed in this work are SVM and random forest with 10-fold cross-validation on three different sets of ECG data acquired for 45 subjects (15 subjects in each age group). The minimum classification accuracies achieved with SVM and RF emotion classifier models are 86.6% and 98.2%, respectively.

show abstract

Section: Related Workmentioning

confidence: 99%

Design and Development of a Non-Contact ECG-Based Human Emotion Recognition System Using SVM and RF Classifiers

2023

View full text Add to dashboard Cite

show abstract

“…The process typically involves converting audio data into spectrograms, which serve as visual representations, which are then analyzed by supervised learning algorithms, particularly CNNs, to identify unique features. In the medical field, Kutsumi et al [32] utilized a CNN to analyze bowel sounds captured by smartphone microphones as a non-invasive measure of gastrointestinal health. Similarly, Peruzzi et al [33] explored the use of acoustics for diagnosing bruxism-related disorders, and Tariq et al [34] leveraged audio data from lung and heart sounds for the classification of various medical conditions, contributing to the advancement of early disease diagnostics across multiple health domains.…”

Section: Classification Based On Audio Datamentioning

confidence: 99%

Bat2Web: A Framework for Real-Time Classification of Bat Species Echolocation Signals Using Audio Sensor Data

Mahbub,

Bhagwagar,

Chand

et al. 2024

Sensors

View full text Add to dashboard Cite

Bats play a pivotal role in maintaining ecological balance, and studying their behaviors offers vital insights into environmental health and aids in conservation efforts. Determining the presence of various bat species in an environment is essential for many bat studies. Specialized audio sensors can be used to record bat echolocation calls that can then be used to identify bat species. However, the complexity of bat calls presents a significant challenge, necessitating expert analysis and extensive time for accurate interpretation. Recent advances in neural networks can help identify bat species automatically from their echolocation calls. Such neural networks can be integrated into a complete end-to-end system that leverages recent internet of things (IoT) technologies with long-range, low-powered communication protocols to implement automated acoustical monitoring. This paper presents the design and implementation of such a system that uses a tiny neural network for interpreting sensor data derived from bat echolocation signals. A highly compact convolutional neural network (CNN) model was developed that demonstrated excellent performance in bat species identification, achieving an F1-score of 0.9578 and an accuracy rate of 97.5%. The neural network was deployed, and its performance was evaluated on various alternative edge devices, including the NVIDIA Jetson Nano and Google Coral.

show abstract

“…Feature extraction was carried out by exploiting Mel-scaled spectrograms for each of the temporal windows. This choice was motivated by the proven effectiveness of this technique when exploited for non-voice audio data, for instance, in the healthcare domain [ 31 , 43 , 44 , 45 ], for sound detection [ 46 ], and in robotic interfaces [ 47 ]. Mel-scaled spectrograms are spectrograms undergoing Mel filterbanks, in which a series of triangular filters reduce the correlation between consecutive frequency bins of the spectrogram to which they are applied.…”

Section: System Overviewmentioning

confidence: 99%

Fight Fire with Fire: Detecting Forest Fires with Embedded Machine Learning Models Dealing with Audio and Images on Low Power IoT Devices

Peruzzi

Pozzebon

Meer

2023

Sensors

View full text Add to dashboard Cite

Forest fires are the main cause of desertification, and they have a disastrous impact on agricultural and forest ecosystems. Modern fire detection and warning systems rely on several techniques: satellite monitoring, sensor networks, image processing, data fusion, etc. Recently, Artificial Intelligence (AI) algorithms have been applied to fire recognition systems, enhancing their efficiency and reliability. However, these devices usually need constant data transmission along with a proper amount of computing power, entailing high costs and energy consumption. This paper presents the prototype of a Video Surveillance Unit (VSU) for recognising and signalling the presence of forest fires by exploiting two embedded Machine Learning (ML) algorithms running on a low power device. The ML models take audio samples and images as their respective inputs, allowing for timely fire detection. The main result is that while the performances of the two models are comparable when they work independently, their joint usage according to the proposed methodology provides a higher accuracy, precision, recall and F1 score (96.15%, 92.30%, 100.00%, and 96.00%, respectively). Eventually, each event is remotely signalled by making use of the Long Range Wide Area Network (LoRaWAN) protocol to ensure that the personnel in charge are able to operate promptly.

show abstract

Automated Bowel Sound and Motility Analysis with CNN Using a Smartphone

Cited by 13 publications

References 24 publications

Design and Development of a Non-Contact ECG-Based Human Emotion Recognition System Using SVM and RF Classifiers

Design and Development of a Non-Contact ECG-Based Human Emotion Recognition System Using SVM and RF Classifiers

Bat2Web: A Framework for Real-Time Classification of Bat Species Echolocation Signals Using Audio Sensor Data

Fight Fire with Fire: Detecting Forest Fires with Embedded Machine Learning Models Dealing with Audio and Images on Low Power IoT Devices

Contact Info

Product

Resources

About