ArticleSensors (Basel, Switzerland)2019
Context-Aware Winter Sports Based on Multivariate Sequence Learning.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Fine-Grained Motion Recognition in At-Home Fitness Monitoring with Smartwatch: A Comparative Analysis of Explainable Deep Neural Networks.Healthcare (Basel, Switzerland) · 2023Article
- Alpine Skiing Activity Recognition Using Smartphone's IMUs.Sensors (Basel, Switzerland) · 2022Article
- Estimation of Fine-Grained Foot Strike Patterns with Wearable Smartwatch Devices.International journal of environmental research and public health · 2022Article
- Sports Action Recognition Based on Deep Learning and Clustering Extraction Algorithm.Computational intelligence and neuroscience · 2022Article
- Recognition of Fine-Grained Walking Patterns Using a Smartwatch with Deep Attentive Neural Networks.Sensors (Basel, Switzerland) · 2021Article
- Article
- Classification of Alpine Skiing Styles Using GNSS and Inertial Measurement Units.Sensors (Basel, Switzerland) · 2020Article
- Knocking and Listening: Learning Mechanical Impulse Response for Understanding Surface Characteristics.Sensors (Basel, Switzerland) · 2020Article
- Lower Limb Kinematics Trajectory Prediction Using Long Short-Term Memory Neural Networks.Frontiers in bioengineering and biotechnology · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this paper, we present an intelligent system that is capable of estimating the status of a player engaging in winter activities based on the sequence analysis of multivariate time-series sensor signals. Among the winter activities, this paper mainly focuses on downhill winter sports such as alpine skiing and snowboarding. Assuming that the mechanical vibrations generated by physical interaction between the ground surface and ski/snowboard in motion can describe the ground conditions and playing contexts, we utilize inertial and vibration signals to categorize the motion context. For example, the proposed system estimates whether the player is sitting on a ski lift or standing on the escalator, or skiing on wet or snowy ground, etc. To measure the movement of a player during a game or on the move, we develop a custom embedded system comprising a motion sensor and piezo transducer. The captured multivariate sequence signals are then trained in a supervised fashion. We adopt artificial neural network approaches (e.g., 1D convolutional neural network, and gated recurrent neural networks, such as long short-term memory and gated recurrent units). The experimental results validate the feasibility of the proposed approach.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.