ImAiR: Airwriting Recognition framework using Image Representation of IMU Signals

The problem of Airwriting Recognition is focused on identifying letters written by movement of finger in free space. It is a type of gesture recognition where the dictionary corresponds to letters in a specific language. In particular, airwriting recognition using sensor data from wrist-worn devices can be used as a medium of user input for applications in Human-Computer Interaction (HCI). Recognition of in-air trajectories using such wrist-worn devices is limited in literature and forms the basis of the current work. In this paper, we propose an airwriting recognition framework by first encoding the time-series data obtained from a wearable Inertial Measurement Unit (IMU) on the wrist as images and then utilizing deep learning-based models for identifying the written alphabets. The signals recorded from 3-axis accelerometer and gyroscope in IMU are encoded as images using different techniques such as Self Similarity Matrix (SSM), Gramian Angular Field (GAF) and Markov Transition Field (MTF) to form two sets of 3-channel images. These are then fed to two separate classification models and letter prediction is made based on an average of the class conditional probabilities obtained from the two models. Several standard model architectures for image classification such as variants of ResNet, DenseNet, VGGNet, AlexNet and GoogleNet have been utilized. Experiments performed on two publicly available datasets demonstrate the efficacy of the proposed strategy. The code for our implementation will be made available at https://github.com/ayushayt/ImAiR

SCLAiR : Supervised Contrastive Learning for User and Device Independent Airwriting Recognition

Airwriting Recognition is the problem of identifying letters written in free space with finger movement. It is essentially a specialized case of gesture recognition, wherein the vocabulary of gestures corresponds to letters as in a particular language. With the wide adoption of smart wearables in the general population, airwriting recognition using motion sensors from a smart-band can be used as a medium of user input for applications in Human-Computer Interaction. There has been limited work in the recognition of in-air trajectories using motion sensors, and the performance of the techniques in the case when the device used to record signals is changed has not been explored hitherto. Motivated by these, a new paradigm for device and user-independent airwriting recognition based on supervised contrastive learning is proposed. A two stage classification strategy is employed, the first of which involves training an encoder network with supervised contrastive loss. In the subsequent stage, a classification head is trained with the encoder weights kept frozen. The efficacy of the proposed method is demonstrated through experiments on a publicly available dataset and also with a dataset recorded in our lab using a different device. Experiments have been performed in both supervised and unsupervised settings and compared against several state-of-the-art domain adaptation techniques. Data and the code for our implementation will be made available at https://github.com/ayushayt/SCLAiR

Modellierung und Erkennung dreidimensionaler Handschrift mittels Inertialsensorik

In dieser Dissertation wird mit Airwriting eine Technologie präsentiert, die eine freihändige, jederzeit verfügbare und leicht erlernbare Texteingabe für Wearable Computing Systeme durch Schreiben in der Luft erlaubt. Die Bewegungserfassung erfolgt mittels am Körper getragener Inertialsensoren. Zusätzlich wird auch die inertialsensorbasierte Erkennung traditioneller, mit einem Stift geschriebener, Schrift behandelt und die gestenbasierte Texteingabe mit einer Gestensteuerung kombiniert

Sensing gestures for business intelligence

The combination of sensor data with analytic techniques is growing in popularity for both practitioners and researchers as an Internet of Things (IoT) offers new opportunities and insights. Organisations are trying to use sensor technologies to derive intelligence and gain a competitive edge in their industries. Obtaining data from sensors might not pose too much of a problem, however subsequent utilisation in meeting an organisation’s decision making can be more problematic. Understanding how sensor data analytics can be undertaken is the first step to deriving business intelligence from front line retail environments. This paper explores the use of the Microsoft Kinect sensor to provide intelligence by identifying and sensing gestures to better understand customer behaviour in the retail space

Preliminary design issues for inertial rings in Ambient Assisted Living applications

A wearable 9dof inertial system able to measure hand posture and movement is presented. The design issues for the deployment of measurement instrumentation based on no-invasive ring-shaped inertial units and of a wireless sensor network by them composed are described. Compromises between the physical and functional proprieties of a wearable device and the requirements for the hardware development are discussed with attention to an handsome design concept aesthetically effective. Techniques of power saving based on an optimized firmware programming are mentioned to realize a performing battery powered system featured by an exhaustive operation time. The printed circuit board (PCB) design rules, the choice of the components and materials, the fusion of inertial data with optical sensors outcomes are also discussed. Previous experience in the field of wearable systems are mentioned in the presentation of the results that emphasize the functional and application potential of a 9dof inertial system integrated in a ring-shaped device. � 2015 IEEE