You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
This paper presents a novel approach, called AirScript, for creating, recognizing and visualizing documents in air. We present a novel algorithm, called 2-DifViz, that converts the hand movements in air (captured by a Myo-armband worn by a user) into a sequence of x, y coordinates on a 2D Cartesian plane, and visualizes them on a canvas. Existing sensor-based approaches either do not provide visual feedback or represent the recognized characters using prefixed templates. In contrast, AirScript stands out by giving freedom of movement to the user, as well as by providing a real-time visual feedback of the written characters, making the interaction natural. AirScript provides a recognition module to predict the content of the document created in air. To do so, we present a novel approach based on deep learning, which uses the sensor data and the visualizations created by 2-DifViz. The recognition module consists of a Convolutional Neural Network (CNN) and two Gated Recurrent Unit (GRU) Networks. The output from these three networks is fused to get the final prediction about the characters written in air. AirScript can be used in highly sophisticated environments like a smart classroom, a smart factory or a smart laboratory, where it would enable people to annotate pieces of texts wherever they want without any reference surface. We have evaluated AirScript against various well-known learning models (HMM, KNN, SVM, etc.) on the data of 12 participants. Evaluation results show that the recognition module of AirScript largely outperforms all of these models by achieving an accuracy of 91.7 in a person independent evaluation and a 96.7 accuracy in a person dependent evaluation.

Cited Abstract(s):
[1]: Human activity recognition (HAR) tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of complex sequences of motor movements, and capturing this temporal dynamics is fundamental for successful HAR. Based on the recent success of recurrent neural networks for time series domains, we propose a generic deep framework for activity recognition based on convolutional and LSTM recurrent units, which: (i) is suitable for multimodal wearable sensors; (ii) can perform sensor fusion naturally; (iii) does not require expert knowledge in designing features; and (iv) explicitly models the temporal dynamics of feature activations. We evaluate our framework on two datasets, one of which has been used in a public activity recognition challenge. Our results show that our framework outperforms competing deep non-recurrent networks on the challenge dataset by 4 on average; outperforming some of the previous reported results by up to 9 . Our results show that the framework can be applied to homogeneous sensor modalities, but can also fuse multimodal sensors to improve performance. We characterise key architectural hyperparameters’ influence on performance to provide insights about their optimisation.

[2]: 

[3]: Virtual reality applications are emerging into various regions of research and entertainment. Although visual and acoustic capabilities are already quite impressive, a wide range of users still criticizes the user interface. Frequently complex and very sensitive input devices are being used, although simple gestures would be preferred. While gesture recognition systems are quite common, see Nintendo's Wii mote, a CAVE has further challenges, as the person can be located in any random position and the gestures are not being performed related to a common fixpoint. Applying an infrared tracking system it is possible to reliably locate the hand and compute 3D trajectories. These are then further analyzed with a Long Short-Term Memory approach, which is able to model sequences of variable length with a higher reliability than HMMs.

[4]: 

[5]: Air-writing refers to writing of linguistic characters or words in a free space by hand or finger movements. Air-writing differs from conventional handwriting; the latter contains the pen-up-pen-down motion, while the former lacks such a delimited sequence of writing events. We address air-writing recognition problems in a pair of companion papers. In Part I, recognition of characters or words is accomplished based on six-degree-of-freedom hand motion data. We address air-writing on two levels: motion characters and motion words. Isolated air-writing characters can be recognized similar to motion gestures although with increased sophistication and variability. For motion word recognition in which letters are connected and superimposed in the same virtual box in space, we build statistical models for words by concatenating clustered ligature models and individual letter models. A hidden Markov model is used for air-writing modeling and recognition. We show that motion data along dimensions beyond a 2-D trajectory can be beneficially discriminative for air-writing recognition. We investigate the relative effectiveness of various feature dimensions of optical and inertial tracking signals and report the attainable recognition performance correspondingly. The proposed system achieves a word error rate of 0.8 for word-based recognition and 1.9 for letter-based recognition. We also subjectively and objectively evaluate the effectiveness of air-writing and compare it with text input using a virtual keyboard. The words per minute of air-writing and virtual keyboard are 5.43 and 8.42, respectively.

[6]: In this paper we introduce a new connectionist approach to on-line handwriting recognition and address in particular the problem of recognizing handwritten whiteboard notes. The approach uses a bidirectional recurrent neural network with the long short-term memory architecture. We use a recently introduced objective function, known as Connectionist Temporal Classification (CTC), that directly trains the network to label unsegmented sequence data. Our new system achieves a word recognition rate of 74.0 , compared with 65.4 using a previously developed HMMbased recognition system.
In opposition to HWRA, handwriting recognition on surface using Deep Learning models like Bidirectional LSTMs [6] , Connectionist Temporal Classifiers [2] , and Multidimentional RNNs [4] have outperformed other baseline models (, [5] ). Similar Deep Learning models, such as Convolutional Neural Networks [1] and LSTMs [3] , have also shown improved results in the domain of gesture recognition.