Boosting Inertial-Based Human Activity Recognition With Transformers

Cho

et al. 2022

Sensors

Inertial-measurement-unit (IMU)-based human activity recognition (HAR) studies have improved their performance owing to the latest classification model. In this study, the conformer, which is a state-of-the-art (SOTA) model in the field of speech recognition, is introduced in HAR to improve the performance of the transformer-based HAR model. The transformer model has a multi-head self-attention structure that can extract temporal dependency well, similar to the recurrent neural network (RNN) series while having higher computational efficiency than the RNN series. However, recent HAR studies have shown good performance by combining an RNN-series and convolutional neural network (CNN) model. Therefore, the performance of the transformer-based HAR study can be improved by adding a CNN layer that extracts local features well. The model that improved these points is the conformer-based-model model. To evaluate the proposed model, WISDM, UCI-HAR, and PAMAP2 datasets were used. A synthetic minority oversampling technique was used for the data augmentation algorithm to improve the dataset. From the experiment, the conformer-based HAR model showed better performance than baseline models: the transformer-based-model and the 1D-CNN HAR models. Moreover, the performance of the proposed algorithm was superior to that of algorithms proposed in recent similar studies which do not use RNN-series.

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Inertial-Measurement-Unit-Based Novel Human Activity Recognition Algorithm Using Conformer

Cho

et al. 2022

Sensors

“…Human Activity Recognition. In recent years, a large body of research for wearable-based human activity recognition is dedicated to learning discriminative features by leveraging various deep neural networks, including convolutional networks, residual networks, autoencoders and Transformers [7,22,26,33,41]. These models are accurate when the number of training instances is sufficiently large, yet such performance is not guaranteed when the labels are scarce.…”

Section: Related Workmentioning

confidence: 99%

What Makes Good Contrastive Learning on Small-Scale Wearable-based Tasks?

Qian¹,

Tian²,

Chen³

2022

Preprint

Self-supervised learning establishes a new paradigm of learning representations with much fewer or even no label annotations. Recently there has been remarkable progress on large-scale contrastive learning models which require substantial computing resources, yet such models are not practically optimal for small-scale tasks. To fill the gap, we aim to study contrastive learning on the wearablebased activity recognition task. Specifically, we conduct an in-depth study of contrastive learning from both algorithmic-level and tasklevel perspectives. For algorithmic-level analysis, we decompose contrastive models into several key components and conduct rigorous experimental evaluations to better understand the efficacy and rationale behind contrastive learning. More importantly, for task-level analysis, we show that the wearable-based signals bring unique challenges and opportunities to existing contrastive models, which cannot be readily solved by existing algorithms. Our thorough empirical studies suggest important practices and shed light on future research challenges. In the meantime, this paper presents an open-source PyTorch library CL-HAR, which can serve as a practical tool for researchers 1 . The library is highly modularized and easy to use, which opens up avenues for exploring novel contrastive models quickly in the future. CCS CONCEPTS• Computing methodologies → Unsupervised learning; Learning settings; Neural networks; • Human-centered computing → Ubiquitous and mobile computing.

“…In [23][24][25], a deep learning approach is used for robot indoor navigation. Human activity recognition [26][27][28] and smartphone location recognition (SLR) [29] algorithms based on ML/DL were shown to improve the accuracy of PDR by using it as a prior [30,31]. SLR was also shown to improve the performance adaptive attitude and heading reference system (AHRS) [32].…”

Section: Introductionmentioning

confidence: 99%

QuadNet: A Hybrid Framework for Quadrotor Dead Reckoning

Shurin

Klein

2022

Sensors

Self Cite

Quadrotor usage is continuously increasing for both civilian and military applications such as surveillance, mapping, and deliveries. Commonly, quadrotors use an inertial navigation system combined with a global navigation satellite systems receiver for outdoor applications and a camera for indoor/outdoor applications. For various reasons, such as lighting conditions or satellite signal blocking, the quadrotor’s navigation solution depends only on the inertial navigation system solution. As a consequence, the navigation solution drifts in time due to errors and noises in the inertial sensor measurements. To handle such situations and bind the solution drift, the quadrotor dead reckoning (QDR) approach utilizes pedestrian dead reckoning principles. To that end, instead of flying the quadrotor in a straight line trajectory, it is flown in a periodic motion, in the vertical plane, to enable peak-to-peak (two local maximum points within the cycle) distance estimation. Although QDR manages to improve the pure inertial navigation solution, it has several shortcomings as it requires calibration before usage, provides only peak-to-peak distance, and does not provide the altitude of the quadrotor. To circumvent these issues, we propose QuadNet, a hybrid framework for quadrotor dead reckoning to estimate the quadrotor’s three-dimensional position vector at any user-defined time rate. As a hybrid approach, QuadNet uses both neural networks and model-based equations during its operation. QuadNet requires only the inertial sensor readings to provide the position vector. Experimental results with DJI’s Matrice 300 quadrotor are provided to show the benefits of using the proposed approach.