scholarly journals PHAROS 2.0—A PHysical Assistant RObot System Improved

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4531 ◽  
Author(s):  
Ester Martinez-Martin ◽  
Angelo Costa ◽  
Miguel Cazorla
Keyword(s):  
Recommender System ◽  
Temporal Evolution ◽  
Vision System ◽  
Physical Activities ◽  
Active Aging ◽  
Robot System ◽  
Time Performance ◽  
Daily Exercise ◽  
Cognitive Benefits ◽  
The One

There are great physical and cognitive benefits for older adults who are engaged in active aging, a process that should involve daily exercise. In our previous work on the PHysical Assistant RObot System (PHAROS), we developed a system that proposed and monitored physical activities. The system used a social robot to analyse, by means of computer vision, the exercise a person was doing. Then, a recommender system analysed the exercise performed and indicated what exercise to perform next. However, the system needed certain improvements. On the one hand, the vision system captured the movement of the person and indicated whether the exercise had been done correctly or not. On the other hand, the recommender system was based purely on a ranking system that did not take into account temporal evolution and preferences. In this work, we propose an evolution of PHAROS, PHAROS 2.0, incorporating improvements in both of the previously mentioned aspects. In the motion capture aspect, we are now able to indicate the degree of completeness of each exercise, identifying the part that has not been done correctly, and a real-time performance correction. In this way, the recommender system receives a greater amount of information and so can more accurately indicate the exercise to be performed. In terms of the recommender system, an algorithm was developed to weigh the performance, temporal evolution and preferences, providing a more accurate recommendation, as well as expanding the recommendation to a batch of exercises, instead of just one.

scholarly journals Effects of a Personalized Fitness Recommender System Using Gamification and Continuous Player Modeling: System Design and Long-Term Validation Study (Preprint)

2020 ◽  
Author(s):  
Zhao Zhao ◽  
Ali Arya ◽  
Rita Orji ◽  
Gerry Chan
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Game Design ◽  
Physical Activities ◽  
Positive Effects ◽  
Significant Difference ◽  
Player Modeling ◽  
The One ◽  
Daily Physical Activities

BACKGROUND Gamification and persuasive games are effective tools to motivate behavior change, particularly to promote daily physical activities. On the one hand, studies have suggested that a <i>one-size-fits-all</i> approach does not work well for persuasive game design. On the other hand, player modeling and recommender systems are increasingly used for personalizing content. However, there are few existing studies on how to build comprehensive player models for personalizing gamified systems, recommending daily physical activities, or the long-term effectiveness of such gamified exercise-promoting systems. OBJECTIVE This paper aims to introduce a gamified, 24/7 fitness assistant system that provides personalized recommendations and generates gamified content targeted at individual users to bridge the aforementioned gaps. This research aims to investigate how to design gamified physical activity interventions to achieve long-term engagement. METHODS We proposed a comprehensive model for gamified fitness recommender systems that uses detailed and dynamic player modeling and wearable-based tracking to provide personalized game features and activity recommendations. Data were collected from 40 participants (23 men and 17 women) who participated in a long-term investigation on the effectiveness of our recommender system that gradually establishes and updates an individual player model (for each unique user) over a period of 60 days. RESULTS Our results showed the feasibility and effectiveness of the proposed system, particularly for generating personalized exercise recommendations using player modeling. There was a statistically significant difference among the 3 groups (full, personalized, and gamified) for overall motivation (<i>F</i><sub>3,36</sub>=22.49; <i>P</i>&lt;.001), satisfaction (<i>F</i><sub>3,36</sub>=22.12; <i>P</i>&lt;.001), and preference (<i>F</i><sub>3,36</sub>=15.0; <i>P</i>&lt;.001), suggesting that both gamification and personalization have positive effects on the levels of motivation, satisfaction, and preference. Furthermore, qualitative results revealed that a customized storyline was the most requested feature, followed by a multiplayer mode, more quality recommendations, a feature for setting and tracking fitness goals, and more location-based features. CONCLUSIONS On the basis of these results and drawing from the gamer modeling literature, we conclude that personalizing recommendations using player modeling and gamification can improve participants’ engagement and motivation toward fitness activities over time. CLINICALTRIAL

scholarly journals Effects of a Personalized Fitness Recommender System Using Gamification and Continuous Player Modeling: System Design and Long-Term Validation Study

10.2196/19968 ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. e19968
Author(s):  
Zhao Zhao ◽  
Ali Arya ◽  
Rita Orji ◽  
Gerry Chan
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Game Design ◽  
Physical Activities ◽  
Positive Effects ◽  
Significant Difference ◽  
Player Modeling ◽  
The One ◽  
Daily Physical Activities

Background Gamification and persuasive games are effective tools to motivate behavior change, particularly to promote daily physical activities. On the one hand, studies have suggested that a one-size-fits-all approach does not work well for persuasive game design. On the other hand, player modeling and recommender systems are increasingly used for personalizing content. However, there are few existing studies on how to build comprehensive player models for personalizing gamified systems, recommending daily physical activities, or the long-term effectiveness of such gamified exercise-promoting systems. Objective This paper aims to introduce a gamified, 24/7 fitness assistant system that provides personalized recommendations and generates gamified content targeted at individual users to bridge the aforementioned gaps. This research aims to investigate how to design gamified physical activity interventions to achieve long-term engagement. Methods We proposed a comprehensive model for gamified fitness recommender systems that uses detailed and dynamic player modeling and wearable-based tracking to provide personalized game features and activity recommendations. Data were collected from 40 participants (23 men and 17 women) who participated in a long-term investigation on the effectiveness of our recommender system that gradually establishes and updates an individual player model (for each unique user) over a period of 60 days. Results Our results showed the feasibility and effectiveness of the proposed system, particularly for generating personalized exercise recommendations using player modeling. There was a statistically significant difference among the 3 groups (full, personalized, and gamified) for overall motivation (F3,36=22.49; P<.001), satisfaction (F3,36=22.12; P<.001), and preference (F3,36=15.0; P<.001), suggesting that both gamification and personalization have positive effects on the levels of motivation, satisfaction, and preference. Furthermore, qualitative results revealed that a customized storyline was the most requested feature, followed by a multiplayer mode, more quality recommendations, a feature for setting and tracking fitness goals, and more location-based features. Conclusions On the basis of these results and drawing from the gamer modeling literature, we conclude that personalizing recommendations using player modeling and gamification can improve participants’ engagement and motivation toward fitness activities over time.

Estimation of Critical Damping in Robot Joints and Identification of the Joint for Design With Most Effective Damping Enhancement

1994 ◽  
Author(s):  
Ahmad A. Smaili ◽  
Muhammad Sannah
Keyword(s):  
Natural Frequency ◽  
System Stability ◽  
Flexible Robot ◽  
Robot System ◽  
Joint Compliance ◽  
Dynamics And Control ◽  
Joint Identification ◽  
The One ◽  
And Control ◽  
Critical Damping

Abstract A major hindrance to dynamics and control of flexible robot manipulators is the deficiency of its inherent damping. Damping enhancement, therefore, should result in lower vibration amplitudes, shorter settling times, and improvement of system stability. Since the bulk of robot vibrations is attributed to joint compliance, it is a prudent strategy to design joints with sufficient inherent damping. In this article, a method is proposed to estimate critical damping at each joint and identify the joint that should be targeted for design with sufficient built-in damping. The target joint identification process requires that a n-joint robot system is divided into n-subsystems. Subsystem i includes the compliance of joint i and the inertia of the succeeding links, joint mechanisms, and payload. An equivalent single degree of freedom torsional model is devised and the natural frequency and critical damping is evaluated for each subsystem. The estimated critical damping at the joints are used to determine the elastodynamic response of the entire robot system from a model that includes joint compliance, shear deformation, rotary inertia, and geometric stiffness. The response revealed the following conclusion: The joint of the manipulator that would result in lower amplitudes of vibrations and shorter settling times when designed with sufficient built-in damping is the one that renders a subsystem whose natural frequency is the lowest of all subsystems comprising the robot.

scholarly journals Improvement in Purity of Healthy Tomato Seeds Using an Image-Based One-Class Classification Method

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2690 ◽  
Author(s):  
Jannat Yasmin ◽  
Santosh Lohumi ◽  
Mohammed Raju Ahmed ◽  
Lalit Mohan Kandpal ◽  
Mohammad Akbar Faqeerzada ◽  
...  
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Light Emitting Diode ◽  
Germination Rate ◽  
Classification Method ◽  
Tomato Seeds ◽  
Significant Difference ◽  
Foreign Materials ◽  
The One ◽  
One Class Classification

The feasibility of a color machine vision technique with the one-class classification method was investigated for the quality assessment of tomato seeds. The health of seeds is an important quality factor that affects their germination rate, which may be affected by seed contamination. Hence, segregation of healthy seeds from diseased and infected seeds, along with foreign materials and broken seeds, is important to improve the final yield. In this study, a custom-built machine vision system containing a color camera with a white light emitting diode (LED) light source was adopted for image acquisition. The one-class classification method was used to identify healthy seeds after extracting the features of the samples. A significant difference was observed between the features of healthy and infected seeds, and foreign materials, implying a certain threshold. The results indicated that tomato seeds can be classified with an accuracy exceeding 97%. The infected tomato seeds indicated a lower germination rate (<10%) compared to healthy seeds, as confirmed by the organic growing media germination test. Thus, identification through image analysis and rapid measurement were observed as useful in discriminating between the quality of tomato seeds in real time.

Social Recommendation Combining Trust Relationship and Distance Metric Factorization

2020 ◽  
Vol 29 (15) ◽  
pp. 2050249
Author(s):  
Ming Ye ◽  
Yuanle Deng
Keyword(s):  
Social Relations ◽  
Recommender System ◽  
Cold Start ◽  
User Preferences ◽  
Dijkstra Algorithm ◽  
Distance Metric ◽  
Social Recommendation ◽  
Trust Relationship ◽  
Potential Factor ◽  
The One

The recommender system predicts user preferences by mining user historical behavior data. This paper proposes a social recommendation combining trust relationship and distance metric factorization. On the one hand, the recommender system has a cold start problem, which can be effectively alleviated by adding social relations. Simultaneously, to improve the problem of sparse trust matrix, we use the Jaccard similarity coefficient and the Dijkstra algorithm to reconstruct the trust matrix and explore the potential user trust relationship. On the other hand, the traditional matrix factorization algorithm is modeled by the user item potential factor dot product, however, it does not satisfy the triangle inequality property and affects the final recommender effect. The primary motivator behind our approach is to combine the best of both worlds, mitigate the inherent weaknesses of each paradigm. Combining the advantages of the two ideas, it has been demonstrated that our algorithm can enhance recommender performance and improve cold start in recommender systems.

Robot Vision System for Human Detection and Action Recognition

2020 ◽  
Vol 24 (3) ◽  
pp. 346-356
Author(s):  
Satoshi Hoshino ◽  
◽  
Kyohei Niimura
Keyword(s):  
Optical Flow ◽  
Real Time ◽  
Action Recognition ◽  
Autonomous Navigation ◽  
Vision System ◽  
Robot Vision ◽  
Human Detection ◽  
Time Performance ◽  
Camera Sensors ◽  
Camera Sensor

Mobile robots equipped with camera sensors are required to perceive humans and their actions for safe autonomous navigation. For simultaneous human detection and action recognition, the real-time performance of the robot vision is an important issue. In this paper, we propose a robot vision system in which original images captured by a camera sensor are described by the optical flow. These images are then used as inputs for the human and action classifications. For the image inputs, two classifiers based on convolutional neural networks are developed. Moreover, we describe a novel detector (a local search window) for clipping partial images around the target human from the original image. Since the camera sensor moves together with the robot, the camera movement has an influence on the calculation of optical flow in the image, which we address by further modifying the optical flow for changes caused by the camera movement. Through the experiments, we show that the robot vision system can detect humans and recognize the action in real time. Furthermore, we show that a moving robot can achieve human detection and action recognition by modifying the optical flow.

Fast and Robust Vision System for Shogi Robot

2015 ◽  
Vol 27 (2) ◽  
pp. 182-190
Author(s):  
Gou Koutaki ◽  
◽  
Keiichi Uchimura
Keyword(s):  
Image Processing ◽  
Vision System ◽  
Low Cost ◽  
Robot Arm ◽  
Image Processing Algorithm ◽  
Robot System ◽  
Cost System ◽  
Robot Systems ◽  
Web Camera ◽  
Lower Frame

<div class=""abs_img""> <img src=""[disp_template_path]/JRM/abst-image/00270002/08.jpg"" width=""150"" />Developed shogi robot system</div> The authors developed a low-cost, safety shogi robot system. A Web camera installed on the lower frame is used to recognize pieces and their positions on the board, after which the game program is played. A robot arm moves a selected piece to the position used in playing a human player. A fast, robust image processing algorithm is needed because a low-cost wide-angle Web camera and robot are used. The authors describe image processing and robot systems, then discuss experiments conducted to verify the feasibility of the proposal, showing that even a low-cost system can be highly reliable. </span>

Sharpe Ratios and Alphas in Continuous Time

2004 ◽  
Vol 39 (1) ◽  
pp. 103-114 ◽  
Author(s):  
Lars Tyge Nielsen ◽  
Maria Vassalou
Keyword(s):  
Continuous Time ◽  
Sharpe Ratio ◽  
Continuous Time Model ◽  
Time Model ◽  
Time Performance ◽  
Optimal Portfolio Selection ◽  
Dynamic Portfolio ◽  
Sharpe Ratios ◽  
The One ◽  
Riskless Asset

AbstractThis paper proposes modified versions of the Sharpe ratio and Jensen's alpha, which are appropriate in a simple continuous-time model. Both are derived from optimal portfolio selection. The modified Sharpe ratio equals the ordinary Sharpe ratio plus half of the volatility of the fund. The modified alpha also differs from the ordinary alpha by a second-moment adjustment. The modified and the ordinary Sharpe ratios may rank funds differently. In particular, if two funds have the same ordinary Sharpe ratio, then the one with the higher volatility will rank higher according to the modified Sharpe ratio. This is justified by the underlying dynamic portfolio theory. Unlike their discrete-time versions, the continuous-time performance measures take into account that it is optimal for investors to change the fractions of their wealth held in the fund vs. the riskless asset over time.

Development and Analysis of a New Specialized Gripper Mechanism for Garment Handling

2013 ◽  
Author(s):  
Loan Le ◽  
Matteo Zoppi ◽  
Michal Jilich ◽  
Raffaello Camoriano ◽  
Dimiter Zlatanov ◽  
...  
Keyword(s):  
Vision System ◽  
A Priori ◽  
Design Solution ◽  
Tactile Sensors ◽  
Robot System ◽  
Work Cell ◽  
Simple Mechanism ◽  
Ongoing Work ◽  
Geometric Knowledge ◽  
Stable Shape

This paper reports ongoing work on the design of a new gripper for garments handling. The development of this device is part of the CloPeMa European Project creating a robot system for automated manipulation of clothing and other textile items. First, we analyze the specificity of the application determining the requirements for the design and functioning of the grasping system. Textiles do not have a stable shape and cannot be manipulated on the basis of a priori geometric knowledge. The necessary exploration of the material and the environment is performed with the help of tactile sensors embedded in the fingertips of the gripper, complementing the vision system of the robotic work cell. The chosen design solution is a simple mechanism able to perform adequately the grasping task and to permit exploratory finger motions. The kinematics and statics of the mechanism are outlined briefly and, in accord with initial experiments, used to validate the design.

A Way of Stepped Frequency Velocity Compensation

2010 ◽  
Vol 108-111 ◽  
pp. 759-764
Author(s):  
Wei Guo Wang ◽  
Xiao Lin Qiao ◽  
Yi Nan Zhao
Keyword(s):  
Prior Knowledge ◽  
Motion Compensation ◽  
Current Speed ◽  
One Dimension ◽  
Improved Method ◽  
Time Performance ◽  
Range Profile ◽  
Stepped Frequency ◽  
The One ◽  
Group Self

This paper introduces the advantage and disadvantage of traditional motion compensation way in the stepped-frequency Pulses radar, and analyzes the motion influence on the one-dimension range profile. On the premise of understanding the current speed compensation algorithm, this paper offers an improved method, which is the new two-step approach. This method reduces the operation and enhances the Real-time performance of system. The characteristic of this method is that proposes the concept of the minimum pulse group self adaptive speed researching range. Even though, we don’t have the premise of speed prior knowledge, it also has a well applying prospect and universality. Comparing to the algorithm previously advanced, it has more extensive applications and practicability.

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