ForceMyography (FMG) is an emerging competitor to surface ElectroMyography (sEMG) for hand gesture recognition. Most of the state-of-the-art research in this area explores different machine learning algorithms or feature engineering to improve hand gesture recognition performance. This paper proposes a novel signal processing pipeline employing a manifold learning method to produce a robust signal representation to boost hand gesture classifiers’ performance. We tested this approach on an FMG dataset collected from nine participants in 3 different data collection sessions with short delays between each. For each participant’s data, the proposed pipeline was applied, and then different classification algorithms were used to evaluate the effect of the pipeline compared to raw FMG signals in hand gesture classification. The results show that incorporating the proposed pipeline reduced variance within the same gesture data and notably maximized variance between different gestures, allowing improved robustness of hand gestures classification performance and consistency across time. On top of that, the pipeline improved the classification accuracy consistently regardless of different classifiers, gaining an average of 5% accuracy improvement.

Dexterous robotic manipulation tasks depend on estimating the state of in-hand objects, particularly their orientation. Although cameras have been traditionally used to estimate the object’s pose, tactile sensors have recently been studied due to their robustness against occlusions. This paper explores tactile data’s temporal information for estimating the orientation of grasped objects. The data from a compliant tactile sensor were collected using different time-window sample sizes and evaluated using neural networks with long short-term memory (LSTM) layers. Our results suggest that using a window of sensor readings improved angle estimation compared to previous works. The best window size of 40 samples achieved an average of 0.0375 for the mean absolute error (MAE) in radians, 0.0030 for the mean squared error (MSE), 0.9074 for the coefficient of determination (R${}^2$), and 0.9094 for the explained variance score (EXP), with no enhancement for larger window sizes. This work illustrates the benefits of temporal information for pose estimation and analyzes the performance behavior with varying window sizes, which can be a basis for future robotic tactile research. Moreover, it can complement underactuated designs and visual pose estimation methods.

Gesture recognition using surface electromyography (sEMG) serves many applications, from human–machine interfaces to prosthesis control. Many features have been adopted to enhance recognition accuracy. However, studies mostly compare features under a prechosen feature window size or a classifier, biased to a specific application. The bias is evident in the reported accuracy drop, around 10%, from offline gesture recognition in experiment settings to real-time clinical environment studies. This paper explores the feature–classifier pairing compatibility for sEMG. We demonstrate that it is the primary determinant of gesture recognition accuracy under various window sizes and normalization ranges, thus removing application bias. The proposed pairing ranking provides a guideline for choosing the proper feature or classifier in future research. For instance, random forest (RF) performed best, with a mean accuracy of around 74.0%; however, it was optimal with the mean absolute value feature (MAV), giving 86.8% accuracy. Additionally, our ranking showed that the proper pairing enables low-computational models to surpass complex ones. The Histogram feature with linear discriminant analysis classifier (HIST-LDA) was the top pair with 88.6% accuracy. We also concluded that a 1250 ms window and a (−1, 1) signal normalization were the optimal procedures for gesture recognition on the used dataset.

The ability to find optimal egress routing is critical for safe and effective firefighting processes. This article presents a novel smoke emulator that can be used to provide this ability in firefighting processes. It has two fundamental components: 1) a sensor network based on the Internet of Things (IoT) and 2) a simplified computational fluid dynamics (CFD) smoke simulator based on Navier-Stokes equations. Supported by IoT, real-time events, such as door opening and window breaking, can be detected, and the relevant information can be used to update the variables in CFD to ensure high accuracy and relevancy. A long short-term memory (LSTM) is employed to evaluate the values of any sensors temporarily malfunctioning. A two-level of A* routing algorithm (Nosrati et al., 2012) has also been developed to optimize the egress routing for evacuees with an aim to minimize the time of smoke exposure. Simulation studies demonstrated that this smoke emulator helps significantly improve the firefighting process's safety and effectiveness.