Personalized Real-Time Monitoring of Amateur Cyclists on Low-End Devices

Brouwer, Mathias De; Ongenae, Femke; Daneels, Glenn; Municio, Esteban; Famaey, Jeroen; Latré, Steven; Turck, Filip De

doi:10.1145/3184558.3191648

Cited by 6 publications

(15 citation statements)

References 21 publications

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“…Various heterogeneous sensor devices can be plugged in into the platform and communicate with the stream reasoning server over wireless technologies, by using an existing IoT platform designed in previous research [2]. The stream reasoning server [3] consists of a running instance of the C-SPARQL RDF stream processing engine [1]. A cycling ontology has been designed to model domain knowledge, rider profiles, other context data and sensor observations.…”

Section: Platform Architecturementioning

confidence: 99%

“…On the Raspberry Pi, a C-SPARQL server is running. Two continuous queries are registered: getQuantityObservationValue to retrieve quantity sensor observations [3], and getTrainingZone, to retrieve the heart rate training zone corresponding to the rider's heart rate (Listing 1). Both queries are executed every 1 second on a window of 5 seconds.…”

Section: Use Case and Demonstrator: Virtual Training Appmentioning

confidence: 99%

“…An example of one of the app screens asking this data is shown in Figure 2a. Using expertise rules of thumb and the Karvonen formula, the user's personalized heart rate training zone boundaries can be determined from this data [3,5]. This enables to personalize training zone feedback for amateur cyclists, who cannot let their physiological profile be determined in expert lab tests, like in professional cycling.…”

Section: Use Case and Demonstrator: Virtual Training Appmentioning

confidence: 99%

“…In amateur cycling, resources are limited compared to professional cycling. Existing real-time sports analytics solutions used by amateur cyclists, such as Strava 3 and TrainingPeaks 4 , only focus on post-processing the sensor data, and sensor platform feedback visualization app data streams stream reasoning server feedback (query results) Figure 1: Architecture of the real-time training feedback platform do not focus on giving real-time personalized feedback. Moreover, they do not allow to integrate with various heterogeneous sensors and background data sources, e.g., domain knowledge and rider profiles [3].…”

Section: Introductionmentioning

confidence: 99%

“…Existing real-time sports analytics solutions used by amateur cyclists, such as Strava 3 and TrainingPeaks 4 , only focus on post-processing the sensor data, and sensor platform feedback visualization app data streams stream reasoning server feedback (query results) Figure 1: Architecture of the real-time training feedback platform do not focus on giving real-time personalized feedback. Moreover, they do not allow to integrate with various heterogeneous sensors and background data sources, e.g., domain knowledge and rider profiles [3]. By using Semantic Web technologies, this can be made possible: sensor & background data can be semantically enriched using ontologies, and stream reasoning techniques can be applied that combine semantic reasoners with stream processing techniques to consolidate & analyze the sensor data streams & available background knowledge [4].…”

Section: Introductionmentioning

confidence: 99%

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Demonstration of a Stream Reasoning Platform on Low-End Devices to Enable Personalized Real-Time Cycling Feedback

Brouwer

Ongenae

Turck

2019

Lecture Notes in Computer Science

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During amateur cycling training, analyzing sensor data in real-time would allow riders to receive immediate feedback on how they are performing, and adapt their training accordingly. In this paper, a solution with Semantic Web technologies is presented that gives such real-time personalized feedback, by integrating the data streams with domain knowledge, rider profiles & other context data. This solution consists of a stream reasoning engine running on a low-end Raspberry Pi device, and a tablet app showing feedback based on the continuous query results. To demonstrate this in a static environment, a virtual training app is presented, allowing a user to simulate an amateur cycling training.

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Section: Platform Architecturementioning

confidence: 99%

Section: Use Case and Demonstrator: Virtual Training Appmentioning

confidence: 99%

Section: Use Case and Demonstrator: Virtual Training Appmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Demonstration of a Stream Reasoning Platform on Low-End Devices to Enable Personalized Real-Time Cycling Feedback

Brouwer

Ongenae

Turck

2019

Lecture Notes in Computer Science

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Pegasos: a framework for the creation of direct mobile coaching feedback systems

Dobiasch,

Oppl,

Stöckl

et al. 2023

J Multimodal User Interfaces

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Feedback is essential for athletes in order to improve their sport performance. Feedback systems try to provide athletes and coaches not only with visualisations of acquired data, but moreover, with insights into—possibly—invisible aspects of their performance. With the widespread adoption of smartphones and the increase in their capabilities, their use as a device for applications of feedback systems is becoming increasingly popular. However, developing mobile feedback systems requires a high level of expertise from researchers and practitioners. The Direct Mobile Coaching model is a design-paradigm for mobile feedback systems. In order to reduce programming efforts, PEGASOS, a framework for creating feedback systems implementing the so-called Direct Mobile Coaching model, is introduced. The paper compares this framework with state-of-the-art research with regard to their ability of providing different variants feedback and offering multimodality to users.

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Continuous Athlete Monitoring in Challenging Cycling Environments Using IoT Technologies

Municio

Daneels

Brouwer

et al. 2019

IEEE Internet Things J.

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IoT-based solutions for sport analytics aim to improve performance, coaching and strategic insights. These factors are especially relevant in cycling, where real-time data should be available anytime, anywhere, even in remote areas where there are no infrastructure-based communication technologies (e.g. LTE, Wi-Fi). In this paper, we present an experience report on the use of state-of-the-art IoT technologies in cycling, where a group of cyclists can form a reliable and energy efficient mesh network to collect and process sensor data in real-time, such as heart rate, speed and location. This data is analyzed in real-time to estimate the performance of each rider and derive instantaneous feedback. Our solution is the first to combine a local body area network to gather the sensor data from the cyclist and a 6TiSCH network to form a multi-hop long-range wireless sensor network in order to provide each bicycle with connectivity to the sink (e.g. a moving car following the cyclists). In this work we present a detailed technical description of this solution, describing its requirements, options and technical challenges. In order to assess such a deployment, we present a large publicly available data-set from different real-world cycling scenarios (mountain road cycle racing and cyclo-cross) which characterizes the performance of the approach, demonstrating its feasibility and evidencing its relevance and promising possibilities in a cycling context for providing low-power communication with reliable performance.

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Personalized Real-Time Monitoring of Amateur Cyclists on Low-End Devices

Cited by 6 publications

References 21 publications

Demonstration of a Stream Reasoning Platform on Low-End Devices to Enable Personalized Real-Time Cycling Feedback

Demonstration of a Stream Reasoning Platform on Low-End Devices to Enable Personalized Real-Time Cycling Feedback

Pegasos: a framework for the creation of direct mobile coaching feedback systems

Continuous Athlete Monitoring in Challenging Cycling Environments Using IoT Technologies

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