1,721,195 research outputs found
Force Sensing Resistor and Evaluation of Technology for Wearable Body Pressure Sensing
No full textWearable technologies are gaining momentum and widespread diffusion. Thanks to devices such as activity trackers, in form of bracelets, watches, or anklets, the end-users are becoming more and more aware of their daily activity routine, posture, and training and can modify their motor-behavior. Activity trackers are prevalently based on inertial sensors such as accelerometers and gyroscopes. Loads we bear with us and the interface pressure they put on our body also affect posture. A contact interface pressure sensing wearable would be beneficial to complement inertial activity trackers. What is precluding force sensing resistors (FSR) to be the next best seller wearable? In this paper, we provide elements to answer this question. We build an FSR based on resistive material (Velostat) and printed conductive ink electrodes on polyethylene terephthalate (PET) substrate; we test its response to pressure in the range 0–2.7 kPa. We present a state-of-the-art review, filtered by the need to identify technologies adequate for wearables. We conclude that the repeatability is the major issue yet unsolved
Ambient Intelligence, Smart Objects and Sensor Networks: practical experiences
No full textAbstract—The recent advances in microelectronics and related
fields have made the dream of intelligent spaces and objects
come true. Reduction of technology costs, power consumption
and form factor is enabling to exploit increased processing
capabilities of sensor nodes and to distribute the intelligence in
the environment. In this paper, significant examples of ongoing
research on smart environments, sensorized tangible
interfaces applied to smart spaces and assistive technologies
are presented. Each of these examples outlines some of the
AmI challenges, such as, in particular, space awareness, smart
space flexibility to include new smart entities and natural
interfaces
RSSI or Time-of-flight for Bluetooth Low Energy based localization? An experimental evaluation
No full textIn this paper, we focus on Bluetooth Low Energy (BLE) and in particular on its use for ranging, starting from the observation that data on RSSI in BLE comes nearly for free. The SDK typically provides to developers an easy way to extract this data and use it to implement their algorithms. However, RSSI based localization techniques have known limits. An alternative information to be used in ranging for localization purposes is Time-of-Flight (ToF). Still, this data is not provided by the BLE API, therefore we propose a practical approach for ToF extraction on top of BLE to be used as alternative to or in combination with RSSI. Furthermore, with the paper, we release the sources of the library used to perform the ToF measurement on BLE, that can be used per se or as input for a localization algorithm. We tested the measurements indoor and outdoor at different distances, both considering Line-of-Sight free or occluded by user body. We conclude evaluating ranging performance, test repeatability and comparing the obtained results with the popular RSSI based approach
Exploiting Neural Audio Codecs for Edge-to-Gateway Speech Processing
No full textNeural Audio Codecs have become powerful tools for audio processing, offering learnable compression methods that balance high compression ratios with perceptual quality. This paper introduces a signal processing system that utilizes the latent space of Neural Audio Codecs for signal reconstruction and feature extraction in edge computing environments. We design a lightweight NAC encoder inspired by SoundStream, optimized for resource-constrained devices. Our evaluation on speech recognition and classification tasks highlights the system's adaptability to Internet of Things applications. The proposed design achieves a 40× audio waveform compression with only a 3% increase in word error rate for transcription tasks and a 94.6% accuracy on end-to-end intent classification, demonstrating its practicality for real-world deployment. Additionally, the encoder operates at a real-time factor of 1.77 on an ARM Cortex-A53 using a single thread for intra/inter-operation, ensuring efficient real-time compression and 12-8 times less energy consumption compared to the original model encoder
Case Study: Gesture and posture recognition using WSN
No full textAdvances in embedded systems have made it possible to design wireless sensor networks that are tiny, low-power, wearable and hence suitable for bio-monitoring. This case study proposes a short overview of possible solutions and uses of an inertial-based wireless sensor node called WiMoCA, (Farella et al. 2005), both for use alone or in a body area network to track gestures and movements for different purposes. Thanks to the flexibility of WiMoCA architecture, it was possible to implement a different node along with the ones known as 3dID glove nodes, dedicated to hand movement tracking. The general scenario is one of ambient intelligence where gestures and movements can be used as natural interfaces for humanmachine
interaction. Moreover, movement, posture and gait tracking may be the keys to understanding user behaviour and thus enabling seamless provision, in a smart environment, of context-aware services such as domotic applications and remote medical monitoring
IMU-integrated Artifact Subspace Reconstruction for Wearable EEG Devices
No full textElectroencephalography (EEG) provides unique insights into natural brain dynamics outside the laboratory setting. However, its usability is limited due to the presence of artifacts. Artifact Subspace Reconstruction (ASR) has been a popular method for enhancing the signal-to-noise ratio (SNR) in mobile EEG; nonetheless, its complexity restricts its applicability on lightweight, resource-constrained EEG devices. To address this challenge, we propose an innovative IMU-integrated approach for artifacts correction (IMU-ASR). Specifically, we replace ASR’s time-consuming calibration process with a simpler accelerometer-based method, significantly reducing computational time without compromising performance. We validate our approach on two publicly available datasets, one with low-density (8 channels) and the other with high-density (120 channels) EEG. Our findings demonstrate the potential of accelerometer-driven ASR for lightweight hardware-software EEG solutions, promising a more practical and efficient approach for artifact correction in mobile EEG applications
Synchronization methods for Bluetooth based WBANs
No full textWireless Body Area Networks (WBANs) can take advantage of many wireless protocols. Among them, Bluetooth is a good candidate since its widespread adoption guarantees compatibility with a number of devices and significantly reduces development time. In most cases data collected from different sensors on different nodes need to be synchronized. We present a synchronization protocol that makes use of Bluetooth piconet internal clock to achieve near-millisecond accuracy with minimal radio communication overhead. Experimental results show that Bluetooth low power modes does not affect negatively accuracy, but improves it, obtaining less power consumption and higher synchronization accuracy
Kinect and wearable inertial sensors for motor rehabilitation programs at home: state of the art and an experimental comparison
Full text linkEmerging sensing and communication technologies are contributing to the development of many motor rehabilitation programs outside the standard healthcare facilities. Nowadays, motor rehabilitation exercises can be easily performed and monitored even at home by a variety of motion-tracking systems. These are cheap, reliable, easy-to-use, and allow also remote configuration and control of the rehabilitation programs. The two most promising technologies for home-based motor rehabilitation programs are inertial wearable sensors and video-based motion capture systems
Aware and Smart Environment: The Casattenta Project
No full text”Casattenta” (Aware home, in Italian) is the demonstrator of a research project on ”Ambient Intelligence”, “Sensor Fusion” and “Wireless Sensor Networks”. The result is a system composed of fixed and wearable sensor nodes, providing elderly people living alone in their house (but also persons in other situations and environments) with adequate and non intrusive monitoring in order to improve the quality of their life. The system consists of fixed smart sensors distributed in the environment and wearable ones monitoring inhabitants health and activity. The interaction between fixed and mobile nodes, based on the ZigBee wireless protocol, allows indoor tracking and identification of dangerous events
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