Preliminary design issues for inertial rings in Ambient Assisted Living applications

Esposito, Dario; Cavallo, Filippo

doi:10.1109/i2mtc.2015.7151274

Cited by 8 publications

(7 citation statements)

References 13 publications

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“…To improve the analysis of the IW combination, future acquisition could be done with a ring like device for the index phalange, like the one presented in [ 41 ] and a custom device for the wrist, which will be more wearable. Usability and acceptability studies could be performed with this configuration of sensors, to analyze the comfort and obtrusiveness of the sensors.…”

Section: Discussionmentioning

confidence: 99%

Recognition of Daily Gestures with Wearable Inertial Rings and Bracelets

Moschetti

Fiorini

Esposito

et al. 2016

Sensors

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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.

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Section: Discussionmentioning

confidence: 99%

Recognition of Daily Gestures with Wearable Inertial Rings and Bracelets

Moschetti

Fiorini

Esposito

et al. 2016

Sensors

Self Cite

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“…We are confident that this issue will be soon overcome, thanks to the recent growing interest for smart jewelry [36], where sensors can be part of already used objects, becoming thus more acceptable. Considering this trend, further development of the SensHand is already in progress with a wireless version made of a bracelet and a small ring [37]. In this way, the system can become less invasive and cumbersome and more usable in daily activities.…”

Section: Discussionmentioning

confidence: 99%

Enhancing Activity Recognition of Self-Localized Robot Through Depth Camera and Wearable Sensors

et al. 2018

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Robots will become part of our everyday life as helpers and companions, sharing the environment with us. Thus robots should become social and able to naturally interact with the users. Recognizing human activities and behaviors will enhance the capabilities of the robot to plan an appropriate action and tailor the approach according to what the user is doing. Therefore, this paper addresses the problem of providing mobile robots with the ability to recognize common daily activities. The fusion of heterogeneous data gathered by multiple sensing strategies, namely wearable inertial sensors, depth camera, and location features, is proposed to improve the recognition of human activity. In particular, the proposed work aims to recognize ten activities using data from a depth camera mounted on a mobile robot able to self-localize in the environment and from customized sensors worn on the hand. Twenty users were asked to perform the selected activities in two different relative positions between them and the robot, while the robot was moving. The analysis was carried out considering different combinations of sensors to evaluate how the fusion of the different technologies improve the recognition abilities. The results show an improvement of 13% in the F-measure when different sensors are considered with respect to the use of the sensors of the robot. In particular, the system is able to recognize not only the performed activity, but also the relative position, enhancing the robot capabilities to interact with the users.

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“…Moreover, the use of an inertial ring, such as the one developed in [32], together with the use of a sensor on the wrist could be further investigated to evaluate the recognition rate of other activities, such as physical activities. In this way, it could be possible to increase the number of recognized activities, increasing the ability of the system to detect changes in daily routine and analyze the lifestyle of people.…”

Section: Discussionmentioning

confidence: 99%

Toward an Unsupervised Approach for Daily Gesture Recognition in Assisted Living Applications

et al. 2017

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Activity Recognition is important in assisted living applications to monitor people at home. Over the past, inertial sensors have been used to recognize different activities, spanning from physical activities to eating ones. Over the last years, supervised methods have been widely used, but they require an extensive labeled dataset to train the algorithms and this may represent a limitation of concrete approaches. This paper presents a comparison of unsupervised and supervised methods in recognizing nine gestures by means of two inertial sensors placed on the index finger and on the wrist. Three supervised classification techniques, namely Random Forest, Support Vector Machine, and Multilayer Perceptron, as well as three unsupervised classification techniques, namely k-Means, Hierarchical Clustering, and Self-Organized Maps, were compared in the recognition of gestures made by 20 subjects. The obtained results show that the Support Vector Machine classifier provided the best performances (0.94 accuracy) compared to the other supervised algorithms. However, the outcomes show that even in an unsupervised context, the system is able to recognize the gestures with an average accuracy of ~0.81. The proposed system may be therefore involved in future telecare services that could monitor the activities of daily living, allowing an unsupervised approach that does not require labelled data

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Preliminary design issues for inertial rings in Ambient Assisted Living applications

Cited by 8 publications

References 13 publications

Recognition of Daily Gestures with Wearable Inertial Rings and Bracelets

Recognition of Daily Gestures with Wearable Inertial Rings and Bracelets

Enhancing Activity Recognition of Self-Localized Robot Through Depth Camera and Wearable Sensors

Toward an Unsupervised Approach for Daily Gesture Recognition in Assisted Living Applications

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