Match!

Recognition of Daily Gestures with Wearable Inertial Rings and Bracelets

Published on Aug 22, 2016in Sensors3.03
· DOI :10.3390/s16081341
Alessandra Moschetti3
Estimated H-index: 3
,
Laura Fiorini7
Estimated H-index: 7
+ 2 AuthorsFilippo Cavallo14
Estimated H-index: 14
Cite
Abstract
Recognition of activities of daily living plays an important role in monitoring elderly people and helping caregivers in controlling and detecting changes in daily behaviors. Thanks to the miniaturization and low cost of Microelectromechanical systems (MEMs), in particular of Inertial Measurement Units, in recent years body-worn activity recognition has gained popularity. In this context, the proposed work aims to recognize nine different gestures involved in daily activities using hand and wrist wearable sensors. Additionally, the analysis was carried out also considering different combinations of wearable sensors, in order to find the best combination in terms of unobtrusiveness and recognition accuracy. In order to achieve the proposed goals, an extensive experimentation was performed in a realistic environment. Twenty users were asked to perform the selected gestures and then the data were off-line analyzed to extract significant features. In order to corroborate the analysis, the classification problem was treated using two different and commonly used supervised machine learning techniques, namely Decision Tree and Support Vector Machine, analyzing both personal model and Leave-One-Subject-Out cross validation. The results obtained from this analysis show that the proposed system is able to recognize the proposed gestures with an accuracy of 89.01% in the Leave-One-Subject-Out cross validation and are therefore promising for further investigation in real life scenarios.
  • References (37)
  • Citations (21)
Cite
References37
Newest
Published on Jun 1, 2016in Sensors3.03
Martin Gjoreski4
Estimated H-index: 4
,
Hristijan Gjoreski13
Estimated H-index: 13
+ 1 AuthorsMatjaz Gams15
Estimated H-index: 15
Although wearable accelerometers can successfully recognize activities and detect falls, their adoption in real life is low because users do not want to wear additional devices. A possible solution is an accelerometer inside a wrist device/smartwatch. However, wrist placement might perform poorly in terms of accuracy due to frequent random movements of the hand. In this paper we perform a thorough, large-scale evaluation of methods for activity recognition and fall detection on four datasets. On...
Published on Apr 28, 2016in Sensors3.03
Shamir Alavi1
Estimated H-index: 1
(Carleton University),
Dennis L. Arsenault2
Estimated H-index: 2
(Carleton University),
Anthony Whitehead11
Estimated H-index: 11
(Carleton University)
This work presents the development and implementation of a unified multi-sensor human motion capture and gesture recognition system that can distinguish between and classify six different gestures. Data was collected from eleven participants using a subset of five wireless motion sensors (inertial measurement units) attached to their arms and upper body from a complete motion capture system. We compare Support Vector Machines and Artificial Neural Networks on the same dataset under two different...
Published on Mar 24, 2016in Sensors3.03
Muhammad Shoaib7
Estimated H-index: 7
,
Stephan Bosch8
Estimated H-index: 8
+ 2 AuthorsPaul J.M. Havinga40
Estimated H-index: 40
The position of on-body motion sensors plays an important role in human activity recognition. Most often, mobile phone sensors at the trouser pocket or an equivalent position are used for this purpose. However, this position is not suitable for recognizing activities that involve hand gestures, such as smoking, eating, drinking coffee and giving a talk. To recognize such activities, wrist-worn motion sensors are used. However, these two positions are mainly used in isolation. To use richer conte...
Published on Feb 4, 2016in Sensors3.03
Lukun Wang1
Estimated H-index: 1
This paper provides an approach for recognizing human activities with wearable sensors. The continuous autoencoder (CAE) as a novel stochastic neural network model is proposed which improves the ability of model continuous data. CAE adds Gaussian random units into the improved sigmoid activation function to extract the features of nonlinear data. In order to shorten the training time, we propose a new fast stochastic gradient descent (FSGD) algorithm to update the gradients of CAE. The reconstru...
Published on Feb 1, 2016
Gary M. Weiss25
Estimated H-index: 25
(Fordham University),
Jessica L. Timko2
Estimated H-index: 2
(Fordham University)
+ 2 AuthorsAndrew J. Schreiber1
Estimated H-index: 1
(Fordham University)
Smartwatches and smartphones contain accelerometers and gyroscopes that sense a user's movements, and can help identify the activity a user is performing. Research into smartphone-based activity recognition has exploded over the past few years, but research into smartwatch-based activity recognition is still in its infancy. In this paper we compare smartwatch and smartphone-based activity recognition, and smartwatches are shown to be capable of identifying specialized hand-based activities, such...
Published on Jan 1, 2016
Raffaele Esposito7
Estimated H-index: 7
(Sant'Anna School of Advanced Studies),
Laura Fiorini7
Estimated H-index: 7
(Sant'Anna School of Advanced Studies)
+ 4 AuthorsPaolo Dario69
Estimated H-index: 69
(Sant'Anna School of Advanced Studies)
Published on Dec 11, 2015in Sensors3.03
Ferhat Attal3
Estimated H-index: 3
,
Samer Mohammed16
Estimated H-index: 16
+ 3 AuthorsYacine Amirat19
Estimated H-index: 19
This paper presents a review of different classification techniques used to recognize human activities from wearable inertial sensor data. Three inertial sensor units were used in this study and were worn by healthy subjects at key points of upper/lower body limbs (chest, right thigh and left ankle). Three main steps describe the activity recognition process: sensors’ placement, data pre-processing and data classification. Four supervised classification techniques namely, k-Nearest Neighbor (k-N...
Published on Dec 1, 2015in Journal of Neuroengineering and Rehabilitation3.58
Fabien Massé5
Estimated H-index: 5
(EPFL: École Polytechnique Fédérale de Lausanne),
Roman R. Gonzenbach11
Estimated H-index: 11
+ 3 AuthorsKamiar Aminian45
Estimated H-index: 45
(EPFL: École Polytechnique Fédérale de Lausanne)
Background Stroke survivors often suffer from mobility deficits. Current clinical evaluation methods, including questionnaires and motor function tests, cannot provide an objective measure of the patients’ mobility in daily life. Physical activity performance in daily-life can be assessed using unobtrusive monitoring, for example with a single sensor module fixed on the trunk. Existing approaches based on inertial sensors have limited performance, particularly in detecting transitions between di...
Published on Nov 16, 2015in Frontiers in Robotics and AI
Michalis Vrigkas5
Estimated H-index: 5
(UoI: University of Ioannina),
Christophoros Nikou21
Estimated H-index: 21
(UoI: University of Ioannina),
Ioannis A. Kakadiaris36
Estimated H-index: 36
(UH: University of Houston)
Recognizing human activities from video sequences or still images is a challenging task due to problems such as background clutter, partial occlusion, changes in scale, viewpoint, lighting, and appearance. Many applications, including video surveillance systems, human-computer interaction, and robotics for human behavior characterization, require a multiple activity recognition system. In this work, we provide a detailed review of recent and state-of-the-art research advances in the field of hum...
Published on Nov 1, 2015in IEEE Journal of Biomedical and Health Informatics4.22
Diane J. Cook50
Estimated H-index: 50
(WSU: Washington State University),
Maureen Schmitter-Edgecombe29
Estimated H-index: 29
(WSU: Washington State University),
Prafulla N. Dawadi8
Estimated H-index: 8
(WSU: Washington State University)
One of the many services that intelligent systems can provide is the ability to analyze the impact of different medical conditions on daily behavior. In this study, we use smart home and wearable sensors to collect data, while ( n = 84) older adults perform complex activities of daily living. We analyze the data using machine learning techniques and reveal that differences between healthy older adults and adults with Parkinson disease not only exist in their activity patterns, but that these ...
Cited By21
Newest
Published on Jan 1, 2018in IEEE Sensors Journal3.08
Feiyu Chen1
Estimated H-index: 1
(ZJU: Zhejiang University),
Honghao Lv1
Estimated H-index: 1
(ZJU: Zhejiang University)
+ 5 AuthorsGeng Yang9
Estimated H-index: 9
(ZJU: Zhejiang University)
As a promising component for body sensor networks, the wearable sensors for hand gesture recognition have increasingly received great attention in recent years. By interpreting human intentions through hand gestures, the natural human-robot interaction can be realized in the smart home where the youth and the elderly can perform hand gestures to control the household robot or the robotic wheelchair. Here, a wearable wrist-worn camera sensor (WristCam) was shown to recognize hand trajectory gestu...
Published on May 1, 2019in Forests2.12
Robert F. Keefe7
Estimated H-index: 7
,
Ann M. Wempe2
Estimated H-index: 2
+ 4 AuthorsChristopher C. Caudill
In this paper, we provide an overview of positioning systems for moving resources in forest and fire management and review the related literature. Emphasis is placed on the accuracy and range of different localization and location-sharing methods, particularly in forested environments and in the absence of conventional cellular or internet connectivity. We then conduct a second review of literature and concepts related to several emerging, broad themes in data science, including the terms locati...
Published on May 15, 2019in IEEE Sensors Journal3.08
Irving Herrera-Luna (Universidad Veracruzana), Ericka Janet Rechy-Ramirez4
Estimated H-index: 4
(Universidad Veracruzana)
+ 1 AuthorsAntonio Marin-Hernandez6
Estimated H-index: 6
(Universidad Veracruzana)
Medical conditions and accidents might cause immobility in certain parts of the body. In order to assist people in the rehabilitation process, sensors obtaining bio-signals from the body have been merged to create assistive technology. This systematic review is focused on presenting the state-of-the-art regarding sensor fusion used in the applications for hand rehabilitation. Articles were searched in four databases: IEEE Xplore, Web of Science, ACM Digital Library, and PubMed. Moreover, PRISMA ...
Published on May 2, 2019 in CHI (Human Factors in Computing Systems)
Gierad Laput13
Estimated H-index: 13
(CMU: Carnegie Mellon University),
Chris Harrison34
Estimated H-index: 34
(CMU: Carnegie Mellon University)
Capturing fine-grained hand activity could make computational experiences more powerful and contextually aware. Indeed, philosopher Immanuel Kant argued, "the hand is the visible part of the brain." However, most prior work has focused on detecting whole-body activities, such as walking, running and bicycling. In this work, we explore the feasibility of sensing hand activities from commodity smartwatches, which are the most practical vehicle for achieving this vision. Our investigations started ...
Published on May 1, 2019
Yunchao Bai (NU: Nanjing University), Libo Zhang3
Estimated H-index: 3
(NU: Nanjing University)
+ 1 AuthorsXianzhong Zhou13
Estimated H-index: 13
(NU: Nanjing University)
A skeleton-based object detection method is proposed to recognize the dynamic gesture. The Kinect depth camera is utilized to capture the skeleton of human beings in the dynamic gesture motions, all gestures of the same dynamic gestures are marked and trained. Then a modified Single Shot MultiBox Detector (SSD) network is adopted to locate the arm in the skeleton images. The dynamic gesture is recognized based on the arm skeleton movements in the motion. To balance the precision and recognition ...
Published on Jan 1, 2019
Sang-Yun Shin (Sejong University), Yong-Won Kang (Sejong University), Yong-Guk Kim7
Estimated H-index: 7
(Sejong University)
Although Radio Control (RC) has been a dominant device for controlling a drone, it is known that a fair amount of training period is required to master it. One way to sidestep such an RC-based control scheme would be utilizing either Kinect or Leap Motion sensor by which the user interacts with a drone more naturally. In such cases, however, the pilot has to hang around the sensor since the operating distance of such sensors is rather short. In this study, we propose a new wearable human-drone i...
Published on Dec 17, 2018in Journal of Sensor and Actuator Networks
Akash Gupta , Adnan Al-Anbuky8
Estimated H-index: 8
,
Peter McNair
Falls in the elderly are a common health issue that can involve severe injuries like hip fractures, requiring considerable medical attention, and subsequent care. Following surgery, physiotherapy is essential for strengthening muscles, mobilizing joints and fostering the return to physical activities. Ideally, physiotherapy programmes would benefit from active home-based monitoring of the elderly patients’ daily activities and exercises. This paper aims at providing a preliminary analysis addres...
Published on Dec 1, 2018in Computers & Industrial Engineering3.52
Margherita Peruzzini9
Estimated H-index: 9
(University of Modena and Reggio Emilia),
Fabio Grandi2
Estimated H-index: 2
(University of Modena and Reggio Emilia),
Marcello Pellicciari13
Estimated H-index: 13
(University of Modena and Reggio Emilia)
Abstract In the context of smart factories, where intelligent machines share data and support enhanced functionalities at a factory level, workers are still seen as spectators rather than active players (Hermann, Pentek, & Otto, 2017). Instead, Industry 4.0 represents a great opportunity for workers to become part of the intelligent system; on one hand, operators can generate data to program machines and optimize the process flows, on the other hand they can receive useful information to support...
Published on Nov 1, 2018in IEEE Sensors Journal3.08
Kai-Chun Liu3
Estimated H-index: 3
(NYMU: National Yang-Ming University),
Chia-Yeh Hsieh2
Estimated H-index: 2
(NYMU: National Yang-Ming University),
Chia-Tai Chan8
Estimated H-index: 8
(NYMU: National Yang-Ming University)
Population aging is one of the general issues of public health over the world. Such demographic shifts pose challenges to healthcare system. Several wearable-based activity monitoring systems have been developed to improve the quality of healthcare and provide monitoring information for health professionals, such as the information of kitchen task, dressing task, food and fluid intake, and medication intake. However, few works pay attention to the housekeeping task, as the housekeeping performan...