874 research outputs found
Sensor Sleeve: Sensing Affective Gestures
We describe the use of textile sensors mounted in a garment sleeve to detect affective gestures. The `Sensor Sleeve' is part of a larger project to explore the role of affect in communications. Pressure activated, capacitive and elasto-resistive sensors are investigated and their relative merits reported on. An implemented application is outlined in which a cellphone receives messages derived from the sleeve's sensors using a Bluetooth interface, and relays the signals as text messages to the user's nominated partner
Biosignalâbased humanâmachine interfaces for assistance and rehabilitation : a survey
As a definition, HumanâMachine Interface (HMI) enables a person to interact with a device. Starting from elementary equipment, the recent development of novel techniques and unobtrusive devices for biosignals monitoring paved the way for a new class of HMIs, which take such biosignals as inputs to control various applications. The current survey aims to review the large literature of the last two decades regarding biosignalâbased HMIs for assistance and rehabilitation to outline stateâofâtheâart and identify emerging technologies and potential future research trends. PubMed and other databases were surveyed by using specific keywords. The found studies were further screened in three levels (title, abstract, fullâtext), and eventually, 144 journal papers and 37 conference papers were included. Four macrocategories were considered to classify the different biosignals used for HMI control: biopotential, muscle mechanical motion, body motion, and their combinations (hybrid systems). The HMIs were also classified according to their target application by considering six categories: prosthetic control, robotic control, virtual reality control, gesture recognition, communication, and smart environment control. An everâgrowing number of publications has been observed over the last years. Most of the studies (about 67%) pertain to the assistive field, while 20% relate to rehabilitation and 13% to assistance and rehabilitation. A moderate increase can be observed in studies focusing on robotic control, prosthetic control, and gesture recognition in the last decade. In contrast, studies on the other targets experienced only a small increase. Biopotentials are no longer the leading control signals, and the use of muscle mechanical motion signals has experienced a considerable rise, especially in prosthetic control. Hybrid technologies are promising, as they could lead to higher performances. However, they also increase HMIsâ complex-ity, so their usefulness should be carefully evaluated for the specific application
Transfer: Cross Modality Knowledge Transfer using Adversarial Networks -- A Study on Gesture Recognition
Knowledge transfer across sensing technology is a novel concept that has been
recently explored in many application domains, including gesture-based human
computer interaction. The main aim is to gather semantic or data driven
information from a source technology to classify / recognize instances of
unseen classes in the target technology. The primary challenge is the
significant difference in dimensionality and distribution of feature sets
between the source and the target technologies. In this paper, we propose
TRANSFER, a generic framework for knowledge transfer between a source and a
target technology. TRANSFER uses a language-based representation of a hand
gesture, which captures a temporal combination of concepts such as handshape,
location, and movement that are semantically related to the meaning of a word.
By utilizing a pre-specified syntactic structure and tokenizer, TRANSFER
segments a hand gesture into tokens and identifies individual components using
a token recognizer. The tokenizer in this language-based recognition system
abstracts the low-level technology-specific characteristics to the machine
interface, enabling the design of a discriminator that learns
technology-invariant features essential for recognition of gestures in both
source and target technologies. We demonstrate the usage of TRANSFER for three
different scenarios: a) transferring knowledge across technology by learning
gesture models from video and recognizing gestures using WiFi, b) transferring
knowledge from video to accelerometer, and d) transferring knowledge from
accelerometer to WiFi signals
Segmentation and Recognition of Eating Gestures from Wrist Motion Using Deep Learning
This research considers training a deep learning neural network for segmenting and classifying eating related gestures from recordings of subjects eating unscripted meals in a cafeteria environment. It is inspired by the recent trend of success in deep learning for solving a wide variety of machine related tasks such as image annotation, classification and segmentation. Image segmentation is a particularly important inspiration, and this work proposes a novel deep learning classifier for segmenting time-series data based on the work done in [25] and [30]. While deep learning has established itself as the state-of-the-art approach in image segmentation, particularly in works such as [2],[25] and [31], very little work has been done for segmenting time-series data using deep learning models.
Wrist mounted IMU sensors such as accelerometers and gyroscopes can record activity from a subject in a free-living environment, while being encapsulated in a watch-like device and thus being inconspicuous. Such a device can be used to monitor eating related activities as well, and is thought to be useful for monitoring energy intake for healthy individuals as well as those afflicted with conditions such as being overweight or obese.
The data set that is used for this research study is known as the Clemson Cafeteria Dataset, available publicly at [14]. It contains data for 276 people eating a meal at the Harcombe Dining Hall at Clemson University, which is a large cafeteria environment. The data includes wrist motion measurements (accelerometer x, y, z; gyroscope yaw, pitch, roll) recorded when the subjects each ate an unscripted meal. Each meal consisted of 1-4 courses, of which 488 were used as part of this research. The ground truth labelings of gestures were created by a set of 18 trained human raters, and consist of labels such as âbiteâ used to indicate when the subject starts to put food in their mouth, and later moves the hand away for more âbitesâ or other activities. Other labels include âdrinkâ for liquid intake, ârestâ for stationary hands and âutensilingâ for actions such as cutting the food into bite size pieces, stirring a liquid or dipping food in sauce among other things. All other activities are labeled as âotherâ by the human raters. Previous work in our group focused on recognizing these gesture types from manually segmented data using hidden Markov models [24],[27]. This thesis builds on that work, by considering a deep learning classifier for automatically segmenting and recognizing gestures.
The neural network classifier proposed as part of this research performs satisfactorily well at recognizing intake gestures, with 79.6% of âbiteâ and 80.7% of âdrinkâ gestures being recognized correctly on average per meal. Overall 77.7% of all gestures were recognized correctly on average per meal, indicating that a deep learning classifier can successfully be used to simultaneously segment
and identify eating gestures from wrist motion measured through IMU sensors
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