DeepVentilation: Learning to Predict Physical Effort from Breathing

Sen, Sagar; Bernabé, Pierre; Husom, Erik Johannes

doi:10.24963/ijcai.2020/753

Cited by 3 publications

(5 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In C2, SZ required data from the Norwegian Institute of Sports Science to create physical and deep learning models to predict respiratory minute ventilation from ribcage movement data. Since the data collection process is cumbersome, the development of the deep learning models was based on maximizing the use of deep learning techniques that can generalize reasonably well from small data and discuss the risks of faulty prediction for unforeseen data [55]. In C3, sensor data from machine tools such as CNC milling, turning, broaching, and grinding is needed to develop tools to validate and improve data quality for Industry 4.0.…”

Section: P13: Anticipate Unavailability Of Datamentioning

confidence: 99%

“…In C2, SZ had the long term goal of developing a mathematical model to predict respiratory flow from ribcage movement data obtained from their Flow sensor. This work required collaboration with the Norwegian School of Sports Science to obtain data to verify the validity of a physical model and also test new techniques in deep learning [55]. Data acquisition from an exercise spirometer and the Flow sensor developed by SZ was a long-term task that was performed in parallel and remotely by the sports school.…”

Section: P14: Define Long-term Tasks That Enable Remote Collaborationmentioning

confidence: 99%

See 1 more Smart Citation

Industry–Academia Research Collaboration and Knowledge Co-creation: Patterns and Anti-patterns

Marijan

Sen

2022

ACM Trans. Softw. Eng. Methodol.

Self Cite

View full text Add to dashboard Cite

Increasing the impact of software engineering research in the software industry and the society at large has long been a concern of high priority for the software engineering community. The problem of two cultures, research conducted in a vacuum (disconnected from the real world), or misaligned time horizons are just some of the many complex challenges standing in the way of successful industry–academia collaborations. This article reports on the experience of research collaboration and knowledge co-creation between industry and academia in software engineering as a way to bridge the research–practice collaboration gap. Our experience spans 14 years of collaboration between researchers in software engineering and the European and Norwegian software and IT industry. Using the participant observation and interview methods, we have collected and afterwards analyzed an extensive record of qualitative data. Drawing upon the findings made and the experience gained, we provide a set of 14 patterns and 14 anti-patterns for industry–academia collaborations, aimed to support other researchers and practitioners in establishing and running research collaboration projects in software engineering.

show abstract

Section: P13: Anticipate Unavailability Of Datamentioning

confidence: 99%

Section: P14: Define Long-term Tasks That Enable Remote Collaborationmentioning

confidence: 99%

Industry–Academia Research Collaboration and Knowledge Co-creation: Patterns and Anti-patterns

Marijan

Sen

2022

ACM Trans. Softw. Eng. Methodol.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Again, changes may be required if any operations are not supported because of differences in library support across models. For example, in [108], the conversion process was accomplished via the conversion of PyTorch to the Onnx standard and then to TensorFlow and TensorFlow.js.…”

Section: Converting Python Models Into Tensorflowjs Modelsmentioning

confidence: 99%

“…Signal analysis A virtual laboratory for EEG data analysis that enables data analysis, pre-processing, and model development was built with TensorFlow.js in [3]. Using realtime sensor measurements of rib-cage movement, a separate clinical study in [108] found that an LSTM network model can predict physical effort by comparing how much air a person breathes in a minute to their perceived workout intensity.…”

Section: Video Image and Signal Analysis Appsmentioning

confidence: 99%

Front-end deep learning web apps development and deployment: a review

Goh

Abas

2022

Appl Intell

View full text Add to dashboard Cite

Machine learning and deep learning models are commonly developed using programming languages such as Python, C++, or R and deployed as web apps delivered from a back-end server or as mobile apps installed from an app store. However, recently front-end technologies and JavaScript libraries, such as TensorFlow.js, have been introduced to make machine learning more accessible to researchers and end-users. Using JavaScript, TensorFlow.js can define, train, and run new or existing, pre-trained machine learning models entirely in the browser from the client-side, which improves the user experience through interaction while preserving privacy. Deep learning models deployed on front-end browsers must be small, have fast inference, and ideally be interactive in real-time. Therefore, the emphasis on development and deployment is different. This paper aims to review the development and deployment of these deep-learning web apps to raise awareness of the recent advancements and encourage more researchers to take advantage of this technology for their own work. First, the rationale behind the deployment stack (front-end, JavaScript, and TensorFlow.js) is discussed. Then, the development approach for obtaining deep learning models that are optimized and suitable for front-end deployment is then described. The article also provides current web applications divided into seven categories to show deep learning potential on the front end. These include web apps for deep learning playground, pose detection and gesture tracking, music and art creation, expression detection and facial recognition, video segmentation, image and signal analysis, healthcare diagnosis, recognition, and identification.

show abstract

“…We have previously investigated how minute ventilation in litres per minute can be estimated from ribcage RIP signals using a machine learning approach called DeepVentilation. 17 Deep learning presents the possibility to take into account individual differences such as age, gender weight, height, variation in the placement of sensors across several different people. It offers the possibility to improve prediction and specificity over time with more availability of data.…”

Section: Related Workmentioning

confidence: 99%

Deep learning to predict power output from respiratory inductive plethysmography data

2022

Self Cite

View full text Add to dashboard Cite

Power output is one of the most accurate methods for measuring exercise intensity during outdoor endurance sports, since it records the actual effect of the work performed by the muscles over time. However, power meters are expensive and are limited to activity forms where it is possible to embed sensors in the propulsion system such as in cycling. We investigate using breathing to estimate power output during exercise, in order to create a portable method for tracking physical effort that is universally applicable in many activity forms. Breathing can be quantified through respiratory inductive plethysmography (RIP), which entails recording the movement of the rib cage and abdomen caused by breathing, and it enables us to have a portable, noninvasive device for measuring breathing. RIP signals, heart rate and power output were recorded during a N-of-1 study of a person performing a set of workouts on a stationary bike. The recorded data were used to build predictive models through deep learning algorithms. A convolutional neural network (CNN) trained on features derived from RIP signals and heart rate obtained a mean absolute percentage error (MAPE) of 0.20 (ie, 20% average error). The model showed promising capability of estimating correct power levels and reactivity to changes in power output, but the accuracy is significantly lower than that of cycling power meters.

show abstract

DeepVentilation: Learning to Predict Physical Effort from Breathing

Cited by 3 publications

References 5 publications

Industry–Academia Research Collaboration and Knowledge Co-creation: Patterns and Anti-patterns

Industry–Academia Research Collaboration and Knowledge Co-creation: Patterns and Anti-patterns

Front-end deep learning web apps development and deployment: a review

Deep learning to predict power output from respiratory inductive plethysmography data

Contact Info

Product

Resources

About