Human Activity Recognition Based on Wearable Sensor Data: A Standardization of the State-of-the-Art

Abstract

Human activity recognition based on wearable sensor data has been an attractive research topic due to its application in areas such as healthcare and smart environments. In this context, many works have presented remarkable results using accelerometer, gyroscope and magnetometer data to represent the activities categories. However, current studies do not consider important issues that lead to skewed results, making it hard to assess the quality of sensor-based human activity recognition and preventing a direct comparison of previous works. These issues include the samples generation processes and the validation protocols used. We emphasize that in other research areas, such as image classification and object detection, these issues are already well-defined, which brings more efforts towards the application. Inspired by this, we conduct an extensive set of experiments that analyze different sample generation processes and validation protocols to indicate the vulnerable points in human activity recognition based on wearable sensor data. For this purpose, we implement and evaluate several top-performance methods, ranging from handcrafted-based approaches to convolutional neural networks. According to our study, most of the experimental evaluations that are currently employed are not adequate to perform the activity recognition in the context of wearable sensor data, in which the recognition accuracy drops considerably when compared to an appropriate evaluation approach. To the best of our knowledge, this is the first study that tackles essential issues that compromise the understanding of the performance in human activity recognition based on wearable sensor data.

References in corpus (4)

• LSTM: A Search Space Odyssey
• ADADELTA: An Adaptive Learning Rate Method
• Deep Learning for Sensor-based Activity Recognition: A Survey
• Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems

Cited by in corpus (10)

• Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
• Attention-based Convolutional Neural Network for Weakly Labeled Human Activities Recognition with Wearable Sensors
• Deep ConvLSTM with self-attention for human activity decoding using wearables
• Human Activity Recognition using Wearable Sensors: Review, Challenges, Evaluation Benchmark
• Role of Data Augmentation Strategies in Knowledge Distillation for Wearable Sensor Data
• Wearable Sensor Data Based Human Activity Recognition using Machine Learning: A new approach
• Too Good To Be True: performance overestimation in (re)current practices for Human Activity Recognition
• Am I fit for this physical activity? Neural embedding of physical conditioning from inertial sensors
• Role of Mixup in Topological Persistence Based Knowledge Distillation for Wearable Sensor Data
• Extended Coopetitive Soft Gating Ensemble