An LSTM-based Descriptor for Human Activities Recognition using IMU Sensors

Ashry, Sara; Elbasiony, Reda; Gomaa, Walid

doi:10.5220/0006902405040511

Cited by 12 publications

(3 citation statements)

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“…For LSTMs, the number of trainable parameters is high compared to other deep learning architectures. As a result, the tuning of LSTM parameters can be challenging [53]. We propose an architecture with a single LSTM layer.…”

Section: D Convolutional Neural Netmentioning

confidence: 99%

From the Laboratory to the Field: IMU-Based Shot and Pass Detection in Football Training and Game Scenarios Using Deep Learning

Stoeve

Schuldhaus

Gamp

et al. 2021

Sensors

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The applicability of sensor-based human activity recognition in sports has been repeatedly shown for laboratory settings. However, the transferability to real-world scenarios cannot be granted due to limitations on data and evaluation methods. On the example of football shot and pass detection against a null class we explore the influence of those factors for real-world event classification in field sports. For this purpose we compare the performance of an established Support Vector Machine (SVM) for laboratory settings from literature to the performance in three evaluation scenarios gradually evolving from laboratory settings to real-world scenarios. In addition, three different types of neural networks, namely a convolutional neural net (CNN), a long short term memory net (LSTM) and a convolutional LSTM (convLSTM) are compared. Results indicate that the SVM is not able to reliably solve the investigated three-class problem. In contrast, all deep learning models reach high classification scores showing the general feasibility of event detection in real-world sports scenarios using deep learning. The maximum performance with a weighted f1-score of 0.93 was reported by the CNN. The study provides valuable insights for sports assessment under practically relevant conditions. In particular, it shows that (1) the discriminative power of established features needs to be reevaluated when real-world conditions are assessed, (2) the selection of an appropriate dataset and evaluation method are both required to evaluate real-world applicability and (3) deep learning-based methods yield promising results for real-world HAR in sports despite high variations in the execution of activities.

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Section: D Convolutional Neural Netmentioning

confidence: 99%

From the Laboratory to the Field: IMU-Based Shot and Pass Detection in Football Training and Game Scenarios Using Deep Learning

Stoeve

Schuldhaus

Gamp

et al. 2021

Sensors

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“…Recently, the Long Short‐Term Memory (LSTM) network and its variant have shown good performance in tackling various sequence modeling tasks. The LSTM deep neural network has been widely used for human activity recognition . An LSTM layer is a recurrent neural network (RNN) layer, which supports time and data series in the network.…”

Section: Location‐related Activity Detection and Positioning In Indoomentioning

confidence: 99%

“…An LSTM layer is a recurrent neural network (RNN) layer, which supports time and data series in the network. The greatest advantage of the RNNs is their capability to take contextual information into consideration when mapping between input and output sequences through hidden layer‐units . LSTM can automatically extract useful features and model the inexplicit criterion.…”

Section: Location‐related Activity Detection and Positioning In Indoomentioning

confidence: 99%

Integration of positioning and activity context information for lifelog in urban city area

Kamiya

et al. 2019

NAVIGATION

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Smartphone‐based Lifelog (automatically annotating the users' daily experience from multisensory streams on smartphones) is in great need. Accurate positioning under any situation is one of the most significant techniques for a desirable Lifelog. This paper proposes to detect location‐related activities and use the activity information to improve positioning accuracy. In the proposed system, a human activity recognition module is developed to extract location‐related activities from multisensory streams of smartphones. After that, the proposed system integrates activity information with PDR‐based positioning results in a context‐based map‐matching framework. The developed system can be used for both outdoor and indoor scenarios. Moreover, the developed indoor positioning method is used to determine the positions of calibration points automatically in an auto‐calibration Wi‐Fi positioning system. The proposed methods achieve 3.1‐m accuracy in outdoor and average 2.2‐m accuracy in indoor situations.

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Statistical Metric-Theoretic Approach to Activity Recognition Based on Accelerometer Data

Gomaa

2019

Advances in Intelligent Systems and Computing

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An LSTM-based Descriptor for Human Activities Recognition using IMU Sensors

Cited by 12 publications

References 0 publications

From the Laboratory to the Field: IMU-Based Shot and Pass Detection in Football Training and Game Scenarios Using Deep Learning

From the Laboratory to the Field: IMU-Based Shot and Pass Detection in Football Training and Game Scenarios Using Deep Learning

Integration of positioning and activity context information for lifelog in urban city area

Statistical Metric-Theoretic Approach to Activity Recognition Based on Accelerometer Data

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