scholarly journals Electronic and Electromechanical Tester of Physiological Sensors

2013 ◽  
pp. 243-261
Author(s):  
E. Sazonov ◽  
T. Haskew ◽  
A. Price ◽  
B. Grace ◽  
A. Dollins
Keyword(s):  
Physiological Sensors

Recent Developments of Ambulatory Assessment Methods

2014 ◽  
Vol 25 (4) ◽  
pp. 279-287 ◽  
Author(s):  
Stefan Hey ◽  
Panagiota Anastasopoulou ◽  
André Bideaux ◽  
Wilhelm Stork
Keyword(s):  
Real Time ◽  
Information And Communication Technologies ◽  
New Technologies ◽  
Psychological Research ◽  
Ambulatory Assessment ◽  
Emotional States ◽  
Recent Developments ◽  
Potential Applications ◽  
Information And Communication ◽  
Physiological Sensors

Ambulatory assessment of emotional states as well as psychophysiological, cognitive and behavioral reactions constitutes an approach, which is increasingly being used in psychological research. Due to new developments in the field of information and communication technologies and an improved application of mobile physiological sensors, various new systems have been introduced. Methods of experience sampling allow to assess dynamic changes of subjective evaluations in real time and new sensor technologies permit a measurement of physiological responses. In addition, new technologies facilitate the interactive assessment of subjective, physiological, and behavioral data in real-time. Here, we describe these recent developments from the perspective of engineering science and discuss potential applications in the field of neuropsychology.

scholarly journals CorrNet: Fine-Grained Emotion Recognition for Video Watching Using Wearable Physiological Sensors

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 52
Author(s):  
Tianyi Zhang ◽  
Abdallah El Ali ◽  
Chen Wang ◽  
Alan Hanjalic ◽  
Pablo Cesar
Keyword(s):  
Emotion Recognition ◽  
Electrodermal Activity ◽  
Short Form ◽  
Recognition Performance ◽  
Binary Classification ◽  
Wearable Sensors ◽  
Recognition Algorithm ◽  
Fine Grained ◽  
Physiological Sensors ◽  
Valence And Arousal

Recognizing user emotions while they watch short-form videos anytime and anywhere is essential for facilitating video content customization and personalization. However, most works either classify a single emotion per video stimuli, or are restricted to static, desktop environments. To address this, we propose a correlation-based emotion recognition algorithm (CorrNet) to recognize the valence and arousal (V-A) of each instance (fine-grained segment of signals) using only wearable, physiological signals (e.g., electrodermal activity, heart rate). CorrNet takes advantage of features both inside each instance (intra-modality features) and between different instances for the same video stimuli (correlation-based features). We first test our approach on an indoor-desktop affect dataset (CASE), and thereafter on an outdoor-mobile affect dataset (MERCA) which we collected using a smart wristband and wearable eyetracker. Results show that for subject-independent binary classification (high-low), CorrNet yields promising recognition accuracies: 76.37% and 74.03% for V-A on CASE, and 70.29% and 68.15% for V-A on MERCA. Our findings show: (1) instance segment lengths between 1–4 s result in highest recognition accuracies (2) accuracies between laboratory-grade and wearable sensors are comparable, even under low sampling rates (≤64 Hz) (3) large amounts of neutral V-A labels, an artifact of continuous affect annotation, result in varied recognition performance.

scholarly journals Computational design and optimization of electro-physiological sensors

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Aditya Shekhar Nittala ◽  
Andreas Karrenbauer ◽  
Arshad Khan ◽  
Tobias Kraus ◽  
Jürgen Steimle
Keyword(s):  
Form Factors ◽  
Computational Design ◽  
Quantitative Agreement ◽  
Physiological Data ◽  
High Signal ◽  
Signal Acquisition ◽  
Design And Optimization ◽  
Graphical Tool ◽  
Small Device ◽  
Physiological Sensors

AbstractElectro-physiological sensing devices are becoming increasingly common in diverse applications. However, designing such sensors in compact form factors and for high-quality signal acquisition is a challenging task even for experts, is typically done using heuristics, and requires extensive training. Our work proposes a computational approach for designing multi-modal electro-physiological sensors. By employing an optimization-based approach alongside an integrated predictive model for multiple modalities, compact sensors can be created which offer an optimal trade-off between high signal quality and small device size. The task is assisted by a graphical tool that allows to easily specify design preferences and to visually analyze the generated designs in real-time, enabling designer-in-the-loop optimization. Experimental results show high quantitative agreement between the prediction of the optimizer and experimentally collected physiological data. They demonstrate that generated designs can achieve an optimal balance between the size of the sensor and its signal acquisition capability, outperforming expert generated solutions.

Trustworthy Architecture for Wireless Body Sensor Network

2015 ◽  
pp. 46-80
Author(s):  
G. R. Kanagachidambaresan
Keyword(s):  
Markov Model ◽  
Sensor Network ◽  
Elderly People ◽  
State Transition ◽  
State Machine ◽  
Sensor Nodes ◽  
Monitoring Scheme ◽  
Body Sensor Network ◽  
Finite State ◽  
Physiological Sensors

Wireless Body Sensor Network is a collection of physiological sensors connected to small embedded machines and transceivers to form a monitoring scheme for patients and elderly people. Intrusion and foolproof routing has become mandatory as the Wireless Body Sensor Network has extended its working range. Trust in Wireless Body Sensor Network is greatly determined by the Encryption key size and Energy of the Node. The Sensor Nodes in Wireless Body Sensor Network is powered by small battery banks which are to be removed and recharged often in some cases. Attack to the implanted node in Wireless Body Sensor Network could harm the patient. Finite State Machine helps in realizing the Trust architecture of the Wireless Body Sensor Network. Markov model helps in predicting the state transition from one state to other. This chapter proposes a Trustworthy architecture for creating a trusted and confidential communication for Wireless Body Sensor Network.

scholarly journals Long-term surgical anaesthesia with isoflurane in human habituated Nile Crocodiles

2017 ◽  
Vol 88 ◽  
Author(s):  
George F. Stegmann ◽  
Catherine J.A. Williams ◽  
Craig Franklin ◽  
Tobias Wang ◽  
Michael Axelsson
Keyword(s):  
Body Temperature ◽  
Body Mass ◽  
Room Temperature ◽  
Environmental Temperature ◽  
Recovery Period ◽  
Physiological Data ◽  
Surgical Incision ◽  
Multiple Factors ◽  
Physiological Sensors

A suitable long-term anaesthetic technique was required for implantation of physiological sensors and telemetric devices in sub-adult Nile crocodiles (Crocodylus niloticus) to allow the collection of physiological data. Five Nile crocodiles with a median body mass of 24 kg were used. After manual capture, they were blindfolded and 0.2 mL (1 mg/mL) medetomidine was administered intramuscularly in four of the animals which had an estimated body mass between 20 kg and 30 kg. One crocodile with an estimated body mass of 50 kg received 0.5 mL. For induction, 5 mL propofol (10 mg/mL) was injected intravenously into the occipital sinus. Additional doses were given when required to ensure adequate anaesthesia. Anaesthesia was maintained with 1.5% isoflurane. Ventilation was controlled. Local anaesthesia was administered for surgical incision and external placement of the radio transmitter. Medetomidine was antagonised with atipamezole at the end of surgery. Median heart rate during surgery was 22 beats/min, at extubation 32 beats per min and 30 beats per min the following day at the same body temperature as under anaesthesia. Median body temperature of the animals increased from 27.3 °C to 27.9 °C during anaesthesia, as room temperature increased from 24.5 °C to 29.0 °C during surgery. Anaesthesia was successfully induced with intramuscular medetomidine and intravenous propofol and was maintained with isoflurane for the placement of telemetric implants. Intraoperative analgesia was supplemented with lidocaine infiltration. Perioperative physiological parameters remained stable and within acceptable clinical limits. Multiple factors appear to influence these variables during the recovery period, including residual anaesthetic effects, environmental temperature and physical activity. 

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