scholarly journals Prediction of state anxiety by machine learning applied to photoplethysmography data

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10448
Author(s):  
David Perpetuini ◽  
Antonio Maria Chiarelli ◽  
Daniela Cardone ◽  
Chiara Filippini ◽  
Sergio Rinella ◽  
...  
Keyword(s):  
Machine Learning ◽  
State Anxiety ◽  
Affective Computing ◽  
Pulse Wave ◽  
Learning Algorithm ◽  
Nervous Activity ◽  
Human Machine Interaction ◽  
Promising Tool ◽  
Custom Made ◽  
Machine Interaction

Background As the human behavior is influenced by both cognition and emotion, affective computing plays a central role in human-machine interaction. Algorithms for emotions recognition are usually based on behavioral analysis or on physiological measurements (e.g., heart rate, blood pressure). Among these physiological signals, pulse wave propagation in the circulatory tree can be assessed through photoplethysmography (PPG), a non-invasive optical technique. Since pulse wave characteristics are influenced by the cardiovascular status, which is affected by the autonomic nervous activity and hence by the psychophysiological state, PPG might encode information about emotional conditions. The capability of a multivariate data-driven approach to estimate state anxiety (SA) of healthy participants from PPG features acquired on the brachial and radial artery was investigated. Methods The machine learning method was based on General Linear Model and supervised learning. PPG was measured employing a custom-made system and SA of the participants was assessed through the State-Trait Anxiety Inventory (STAI-Y) test. Results A leave-one-out cross-validation framework showed a good correlation between STAI-Y score and the SA predicted by the machine learning algorithm (r = 0.81; p = 1.87∙10−9). The preliminary results suggested that PPG can be a promising tool for emotions recognition, convenient for human-machine interaction applications.

Design and Validation of a Compatible 3-Degrees of Freedom Shoulder Exoskeleton With an Adaptive Center of Rotation

2014 ◽  
Vol 136 (7) ◽  
Author(s):  
Hua Yan ◽  
Canjun Yang ◽  
Yansong Zhang ◽  
Yiqi Wang
Keyword(s):  
Soft Tissues ◽  
Dimensional Space ◽  
Interaction Force ◽  
Shoulder Abduction ◽  
Upper Arm ◽  
Human Machine Interaction ◽  
Center Of Rotation ◽  
Custom Made ◽  
Flexion Extension ◽  
Machine Interaction

This paper outlines an experimentally based design method for a compatible 3-DOF shoulder exoskeleton with an adaptive center of rotation (CoR) by matching the mechanical CoR with the anatomical CoR to reduce human–machine interaction forces and improve comfort during dynamic humeral motion. The spatial–temporal description for anatomical CoR motion is obtained via a specific experimental task conducted on six healthy subjects. The task is comprised of a static section and a dynamic section, both of which are recorded with an infrared motion capture system using body-attached markers. To reduce the influence of human soft tissues, a custom-made four-marker group block was placed on the upper arm instead of using discrete markers. In the static section, the position of anatomical CoR is kept stationary and calculated using a well-known functional method. Based on the static results, the dynamic section determines the statistical relationship between the dynamic CoR position and the humeral orientation using an optimization method when subjects move their upper arm freely in the sagittal and coronal planes. Based on the resolved anatomical CoR motion, a new mechanical CoR model derived from a traditional ball-and-socket joint is applied to match the experimental results as closely as possible. In this mechanical model, the CoR motion in three-dimensional space is adjusted by translating two of the three intersecting joint axes, including the shoulder abduction/adduction and flexion/extension. A set of optimal translation parameters is obtained through proper matching criterion for the two CoRs. Based on the translation parameters, a compatible shoulder exoskeleton was manufactured and compared with a traditional shoulder exoskeleton with a fixed CoR. An experimental test was conducted to validate the CoR motion adaptation ability by measuring the human–machine interaction force during passive shoulder joint motion. The results provide a promising direction for future anthropomorphic shoulder exoskeleton design.

scholarly journals Phase Recognition of Lung Cancer via Steerable Riesz Wavelets with Rf Algorithm

2020 ◽  
Vol 9 (8) ◽  
pp. 491-496
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Feature Extraction ◽  
Mortality Rate ◽  
Learning Algorithm ◽  
Classification Model ◽  
Good Prediction ◽  
Texture Model ◽  
Promising Tool ◽  
Effectiveness Of Training

Lung cancer is one of the diseases which has a high mortality. If the condition is detected earlier, then it is easier to reduce the mortality rate. This lung cancer has caused more deaths in the world than any other cancer. The main objective is to predict lung cancer using a machine learning algorithm. Several computer-aided systems have been designed to reduce the mortality rate due to lung cancer. Machine learning is a promising tool to predict lung cancer in its early phase or stage, where the features of images are trained using a classification model. Generally, machine learning is used to have a good prediction, but in some models, due to lack of efficient feature extraction value, the training has not been done more effectively; hence the predictions are poor. In order to overcome this limitation, the proposed covariant texture model utilizing the steerable Riesz wavelets feature extraction technique to increase the effectiveness of training via the Random Forest algorithm. In this proposed model, the RF algorithm is employed to predict whether the nodule in the image is benign or malignant ii) to find the level of severity (1 to 5), if it is a malignant nodule. Our experiment result can be used as a tool to support the diagnosis and to analyze at an earlier stage of cancer to cure it.

scholarly journals IOT based Advanced Medicine Dispenser Integrated with an Interactive Web Application

2018 ◽  
Vol 7 (4.10) ◽  
pp. 46
Author(s):  
Nanda Kishor Panda ◽  
Shubham Bhardwaj ◽  
H. Bharadwaj ◽  
Rohil Singhvi
Keyword(s):  
Machine Learning ◽  
Web Application ◽  
The Internet ◽  
Human Machine Interaction ◽  
Everyday Objects ◽  
Front End ◽  
Healthcare Monitoring ◽  
User Friendly ◽  
Machine Interaction ◽  
Interactive User

Internet of Things (IOT) is a development of the internet which plays a  major role in integrating human-machine interaction by allowing everyday objects to send and receive data in a variety of applications. Using IOT in healthcare monitoring provides an avenue for doctors and patients to interact and to track the dosage of medication administered. The paper presents an interactive, user friendly network integrated with an automated medicine dispenser which uses IOT, cloud computing and machine learning. The network was built on a python tornado framework with a front end developed using materialise CSS. The feasibility of this approach was validated by building a prototype and conducting a survey.  

scholarly journals Emotion Recognition for Human-Robot Interaction: Recent Advances and Future Perspectives

2020 ◽  
Vol 7 ◽  
Author(s):  
Matteo Spezialetti ◽  
Giuseppe Placidi ◽  
Silvia Rossi
Keyword(s):  
Emotion Recognition ◽  
Affective Computing ◽  
Brain Activity ◽  
Human Robot Interaction ◽  
Point Of View ◽  
Robot Interaction ◽  
Human Machine Interaction ◽  
Recent Advances ◽  
Critical Issues ◽  
Machine Interaction

A fascinating challenge in the field of human–robot interaction is the possibility to endow robots with emotional intelligence in order to make the interaction more intuitive, genuine, and natural. To achieve this, a critical point is the capability of the robot to infer and interpret human emotions. Emotion recognition has been widely explored in the broader fields of human–machine interaction and affective computing. Here, we report recent advances in emotion recognition, with particular regard to the human–robot interaction context. Our aim is to review the state of the art of currently adopted emotional models, interaction modalities, and classification strategies and offer our point of view on future developments and critical issues. We focus on facial expressions, body poses and kinematics, voice, brain activity, and peripheral physiological responses, also providing a list of available datasets containing data from these modalities.

scholarly journals HOLMeS: eHealth in the Big Data and Deep Learning Era

2018 ◽  
Author(s):  
Flora Amato ◽  
Stefano Marrone ◽  
Vincenzo Moscato ◽  
Gabriele Piantadosi ◽  
Antonio Picariello ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Cluster Computing ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Learning Approaches ◽  
Human Machine Interaction ◽  
Novel Technologies ◽  
Area Under Roc Curve ◽  
Chat Bot

Data collection and analysis are becoming more and more important in a variety of application domains as long as the novel technologies advance. At the same time, we are experiencing a growing need for human-machine interaction with expert systems pushing research through new knowledge representation models and interaction paradigms. In particular, in the last years eHealth - that indicates all the health-care practices supported by electronic elaboration and remote communications - calls for the availability of smart environment and big computational resources. The aim of this paper is to introduce the HOLMeS (Health On-Line Medical Suggestions) framework. The introduced system proposes to change the eHealth paradigm where a trained machine learning algorithm, deployed on a cluster-computing environment, provides medical suggestion via both chat-bot and web-app modules. The chat-bot, based on deep learning approaches, is able to overcome the limitation of biased interaction between users and software, exhibiting a human-like behavior. Results demonstrate the effectiveness of the machine learning algorithms showing 74.65% of Area Under ROC Curve (AUC) when first-level features are used to assess the occurrence of different prevention pathways. When disease-specific features are added, HOLMeS shows 86.78% of AUC achieving a more specific prevention pathway evaluation.

scholarly journals Machine Vision Enabled Bot for Object Tracking

2020 ◽  
Vol 1 (1) ◽  
pp. 15-26
Author(s):  
Rupali Patil ◽  
Adhish Velingkar ◽  
Mohammad Nomaan Parmar ◽  
Shubham Khandhar ◽  
Bhavin Prajapati
Keyword(s):  
Machine Learning ◽  
Object Tracking ◽  
Cloud Storage ◽  
Object Motion ◽  
Human Machine Interaction ◽  
Camera Control ◽  
Live Feed ◽  
Detection And Tracking ◽  
Left And Right ◽  
Machine Interaction

Object detection and tracking are essential and testing undertaking in numerous PC vision appliances. To distinguish the object first find a way to accumulate information. In this design, the robot can distinguish the item and track it just as it can turn left and right position and afterward push ahead and in reverse contingent on the object motion. It keeps up the consistent separation between the item and the robot. We have designed a webpage that is used to display a live feed from the camera and the camera can be controlled by the user efficiently. Implementation of machine learning is done for detection purposes along with open cv and creating cloud storage. The pan-tilt mechanism is used for camera control which is attached to our 3-wheel chassis robot through servo motors. This idea can be used for surveillance purposes, monitoring local stuff, and human-machine interaction.

scholarly journals Human-aided artificial intelligence: Or, how to run large computations in human brains? Toward a media sociology of machine learning

2019 ◽  
Vol 22 (10) ◽  
pp. 1868-1884 ◽  
Author(s):  
Rainer Mühlhoff
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cognitive Abilities ◽  
Human Machine Interaction ◽  
Technological Transformation ◽  
Media Sociology ◽  
Media Technologies ◽  
Media Philosophy ◽  
Human Brains ◽  
Machine Interaction

Today, artificial intelligence (AI), especially machine learning, is structurally dependent on human participation. Technologies such as deep learning (DL) leverage networked media infrastructures and human-machine interaction designs to harness users to provide training and verification data. The emergence of DL is therefore based on a fundamental socio-technological transformation of the relationship between humans and machines. Rather than simulating human intelligence, DL-based AIs capture human cognitive abilities, so they are hybrid human-machine apparatuses. From a perspective of media philosophy and social-theoretical critique, I differentiate five types of “media technologies of capture” in AI apparatuses and analyze them as forms of power relations between humans and machines. Finally, I argue that the current hype about AI implies a relational and distributed understanding of (human/artificial) intelligence, which I categorize under the term “cybernetic AI.” This form of AI manifests in socio-technological apparatuses that involve new modes of subjectivation, social control, and digital labor.

scholarly journals Self-Powered Intelligent Human-Machine Interaction for Handwriting Recognition

Research ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Hang Guo ◽  
Ji Wan ◽  
Haobin Wang ◽  
Hanxiang Wu ◽  
Chen Xu ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Human Computer Interaction ◽  
Supervised Machine Learning ◽  
Triboelectric Nanogenerator ◽  
Human Machine Interaction ◽  
The Neural Network ◽  
Interaction Interface ◽  
Machine Interaction ◽  
Computer Interaction

Handwritten signatures widely exist in our daily lives. The main challenge of signal recognition on handwriting is in the development of approaches to obtain information effectively. External mechanical signals can be easily detected by triboelectric nanogenerators which can provide immediate opportunities for building new types of active sensors capable of recording handwritten signals. In this work, we report an intelligent human-machine interaction interface based on a triboelectric nanogenerator. Using the horizontal-vertical symmetrical electrode array, the handwritten triboelectric signal can be recorded without external energy supply. Combined with supervised machine learning methods, it can successfully recognize handwritten English letters, Chinese characters, and Arabic numerals. The principal component analysis algorithm preprocesses the triboelectric signal data to reduce the complexity of the neural network in the machine learning process. Further, it can realize the anticounterfeiting recognition of writing habits by controlling the samples input to the neural network. The results show that the intelligent human-computer interaction interface has broad application prospects in signature security and human-computer interaction.

scholarly journals Speeding up quantum dissipative dynamics of open systems with kernel methods

2021 ◽  
Author(s):  
Arif Ullah ◽  
Pavlo O. Dral
Keyword(s):  
Machine Learning ◽  
Kernel Methods ◽  
Open Systems ◽  
Learning Algorithm ◽  
Computational Effort ◽  
Time Dynamics ◽  
Dissipative Dynamics ◽  
Promising Tool ◽  
Long Time ◽  
Boson Model

The future forecasting ability of machine learning (ML) makes ML a promising tool for predicting long-time quantum dissipative dynamics of open systems. In this Article, we employ nonparametric machine learning algorithm (kernel ridge regression as a representative of the kernel methods) to study the quantum dissipative dynamics of the widely-used spin-boson model. Our ML model takes short-time dynamics as an input and is used for fast propagation of the long-time dynamics, greatly reducing the computational effort in comparison with the traditional approaches. Presented results show that the ML model performs well in both symmetric and asymmetric spin-boson models. Our approach is not limited to spin-boson model and can be extended to complex systems.

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