scholarly journals THE IMPLEMENTATION OF HEART RATE SENSOR AND MOTION SENSORS BASED ON INTERNET OF THINGS FOR ATLETE PERFORMANCE MONITORING

Kursor ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 196
Author(s):  
Muhammad Aksa Hidayat ◽  
Sritrusta Sukaridhoto ◽  
Achmad Basuki ◽  
Udin Harun Al Rasyid ◽  
Ika Fadhila Aryanti ◽  
...  
Keyword(s):  
Heart Rate ◽  
Internet Of Things ◽  
Motion Capture ◽  
Performance Monitoring ◽  
Sensor Data ◽  
Raspberry Pi ◽  
Motion Sensors ◽  
Web Based ◽  
Rate Sensor ◽  
The Internet Of Things

Indonesian achievements in the ASEAN Games continued to decline in achievement starting in 1962 with the acquisition of 51 medals and up to 2014 with the acquisition of 20 medals. The decline in achievement was due to the lack of athletic resources due to the absence of media that could record athletes' abilities in the field. Can record the athlete's performance before running, running and after running using the Heart Rate sensor and Motion Capture sensor. The results of the sensor recording will be stored in the database. This system applies the Internet of Things (IoT) concept, using raspberry pi, Arduino microcontroller, T34 polar heart rate sensor to capture and send heartbeat to receivers, gyro-based motion-capture sensors that named wear notch where this sensor serves to capture the movement of athletes, sensors communicate with the system using 4G connectivity, use MQTT as edge computing which acts as a communication medium from sensors to databases, Maria DB and influx DB as accumulation which plays a role in storing heart rate and athlete's movements that have been recorded by sensors, athlete performance monitoring platform with a heart rate sensor and athlete's motion capture is a web-based application that collaborates all processes from the sensor to the system. Sensor heart rate recording results are categorized good because the error margin is only 0.4%. Wearnotch sensor data can be stored in the database, and athletic data can be recorded before sports, while sports, and after sports in real-time

scholarly journals A Development of the Internet of Things based Intruder Detection and Security Alarm System

2019 ◽  
Vol 8 (6) ◽  
pp. 2307-2312 ◽  
Keyword(s):  
Internet Of Things ◽  
Smartphone Application ◽  
Experimental Results ◽  
Raspberry Pi ◽  
Average Score ◽  
The Internet ◽  
Alarm System ◽  
Motion Sensors ◽  
The Face ◽  
The Internet Of Things

Intruders usually break into houses with the intention of committing burglaries. This research proposed a development of an intrusion detection and security Alarm System using the Internet of Things. The methodology of the proposed system consists of five components. First, the hardware components are Raspberry Pi 3 Model B+, a camera for Raspberry Pi, motion sensors, relays and speakers, while the software system was developed by Python. Second, the architecture of the proposed system. Third, the design and construction of the electronic circuit connected with sensors. Fourth, the intruder image analysis for the alarm system using OpenCV and Deep Learning. The face detected by the camera was compared with homeowner’s pictures. If the detected face was not the homeowner, the system alarms the user or the owner via the smartphone LINE Application. Last, the Anto, which is the free and easy Internet of Things platform, connect the devices and the smartphone application together via the internet. Hence, the users or homeowners can control the devices or take the picture from a distance using a smartphone. The experimental results show that the proposed system can detect the intruder and alarm the homeowner via LINE Application on the smartphone. The experimental results show that the proposed system can efficiently detect the intruder and alarm the homeowner via LINE Application on the smartphone. The performance of the proposed system is excellent with the average score of 97.40%. The developed application on the Android smartphone is user-friendly, simple and efficient as well.

scholarly journals An Improved Unsupervised Single-Channel Speech Separation Algorithm for Processing Speech Sensor Signals

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Dazhi Jiang ◽  
Zhihui He ◽  
Yingqing Lin ◽  
Yifei Chen ◽  
Linyan Xu
Keyword(s):  
Internet Of Things ◽  
Speech Signal ◽  
Single Channel ◽  
Low Noise ◽  
Sensor Data ◽  
The Internet ◽  
Speech Separation ◽  
Adaptive Wavelet ◽  
Enhancement Technique ◽  
The Internet Of Things

As network supporting devices and sensors in the Internet of Things are leaping forward, countless real-world data will be generated for human intelligent applications. Speech sensor networks, an important part of the Internet of Things, have numerous application needs. Indeed, the sensor data can further help intelligent applications to provide higher quality services, whereas this data may involve considerable noise data. Accordingly, speech signal processing method should be urgently implemented to acquire low-noise and effective speech data. Blind source separation and enhancement technique refer to one of the representative methods. However, in the unsupervised complex environment, in the only presence of a single-channel signal, many technical challenges are imposed on achieving single-channel and multiperson mixed speech separation. For this reason, this study develops an unsupervised speech separation method CNMF+JADE, i.e., a hybrid method combined with Convolutional Non-Negative Matrix Factorization and Joint Approximative Diagonalization of Eigenmatrix. Moreover, an adaptive wavelet transform-based speech enhancement technique is proposed, capable of adaptively and effectively enhancing the separated speech signal. The proposed method is aimed at yielding a general and efficient speech processing algorithm for the data acquired by speech sensors. As revealed from the experimental results, in the TIMIT speech sources, the proposed method can effectively extract the target speaker from the mixed speech with a tiny training sample. The algorithm is highly general and robust, capable of technically supporting the processing of speech signal acquired by most speech sensors.

Towards Autonomous IoT Logistics Objects

2017 ◽  
pp. 210-222
Author(s):  
Bill Karakostas
Keyword(s):  
Internet Of Things ◽  
System Performance ◽  
Credit Card ◽  
Raspberry Pi ◽  
The Internet ◽  
Simulated Environment ◽  
Embedded Knowledge ◽  
New Knowledge ◽  
The Internet Of Things ◽  
Computing Infrastructure

To improve the overall impact of the Internet of Things (IoT), intelligent capabilities must be developed at the edge of the IoT ‘Cloud.' ‘Smart' IoT objects must not only communicate with their environment, but also use embedded knowledge to interpret signals, and by making inferences augment their knowledge of their own state and that of their environment. Thus, intelligent IoT objects must improve their capabilities to make autonomous decisions without reliance to external computing infrastructure. In this chapter, we illustrate the concept of smart autonomous logistic objects with a proof of concept prototype built using an embedded version of the Prolog language, running on a Raspberry Pi credit-card-sized single-board computer to which an RFID reader is attached. The intelligent object is combining the RFID readings from its environment with embedded knowledge to infer new knowledge about its status. We test the system performance in a simulated environment consisting of logistics objects.

scholarly journals A Novel Sensor Data Pre-Processing Methodology for the Internet of Things Using Anomaly Detection and Transfer-By-Subspace-Similarity Transformation

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4536 ◽  
Author(s):  
Yan Zhong ◽  
Simon Fong ◽  
Shimin Hu ◽  
Raymond Wong ◽  
Weiwei Lin
Keyword(s):  
Data Mining ◽  
Internet Of Things ◽  
Anomaly Detection ◽  
Sensor Data ◽  
Detection Methods ◽  
The Internet ◽  
Quality Of Data ◽  
Data Mining Algorithms ◽  
Sensing Applications ◽  
The Internet Of Things

The Internet of Things (IoT) and sensors are becoming increasingly popular, especially in monitoring large and ambient environments. Applications that embrace IoT and sensors often require mining the data feeds that are collected at frequent intervals for intelligence. Despite the fact that such sensor data are massive, most of the data contents are identical and repetitive; for example, human traffic in a park at night. Most of the traditional classification algorithms were originally formulated decades ago, and they were not designed to handle such sensor data effectively. Hence, the performance of the learned model is often poor because of the small granularity in classification and the sporadic patterns in the data. To improve the quality of data mining from the IoT data, a new pre-processing methodology based on subspace similarity detection is proposed. Our method can be well integrated with traditional data mining algorithms and anomaly detection methods. The pre-processing method is flexible for handling similar kinds of sensor data that are sporadic in nature that exist in many ambient sensing applications. The proposed methodology is evaluated by extensive experiment with a collection of classical data mining models. An improvement over the precision rate is shown by using the proposed method.

Future Aspects and Research Perspectives of the Internet of Things

2021 ◽  
pp. 1-18
Author(s):  
Harshit Bhardwaj ◽  
Pradeep Tomar ◽  
Aditi Sakalle ◽  
Taranjeet Singh ◽  
Divya Acharya ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Fog Computing ◽  
The Internet ◽  
Literature Analysis ◽  
Healthcare Applications ◽  
Web Based ◽  
Iot Applications ◽  
Research Perspectives ◽  
Software Production ◽  
The Internet Of Things

Fog computing has latency, particularly for healthcare applications, which is of the utmost importance. This research aims to be a comprehensive literature analysis of healthcare innovations for fog computing. All of these components involved special abilities. In sequence, developers must be qualified to write stable, healthy IoT programs in four distinct fields of software production: embedded, server, tablet, and web-based. Furthermore, the distributed results, IoT structure essence, dispersed abilities in programming play a deciding position. This chapter discusses the difficulties in creating the IoT method and summarizing findings and observations. Experiences of the need for and co-presence of various kinds of skills in software creation in the construction of IoT applications are discussed.

Web-Based Management of the Internet of Things

2015 ◽  
Vol 19 (4) ◽  
pp. 60-67 ◽  
Author(s):  
Lina Yao ◽  
Quan Z. Sheng ◽  
Schahram Dustdar
Keyword(s):  
Internet Of Things ◽  
The Internet ◽  
Web Based ◽  
The Internet Of Things

scholarly journals Predictive maintenance as an internet of things enabled business model: A taxonomy

2020 ◽  
Author(s):  
Jens Passlick ◽  
Sonja Dreyer ◽  
Daniel Olivotti ◽  
Lukas Grützner ◽  
Dennis Eilers ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Internet Of Things ◽  
Manufacturing Industry ◽  
Business Models ◽  
Predictive Maintenance ◽  
Sensor Data ◽  
Visualization Technique ◽  
Hardware Development ◽  
The Internet Of Things ◽  
Different Levels

Abstract Predictive maintenance (PdM) is an important application of the Internet of Things (IoT) discussed in many companies, especially in the manufacturing industry. PdM uses data, usually sensor data, to optimize maintenance activities. We develop a taxonomy to classify PdM business models that enables a comparison and analysis of such models. We use our taxonomy to classify the business models of 113 companies. Based on this classification, we identify six archetypes using cluster analysis and discuss the results. The “hardware development”, “analytics provider”, and “all-in-one” archetypes are the most frequently represented in the study sample. For cluster analysis, we use a visualization technique that involves an autoencoder. The results of our analysis will help practitioners assess their own business models and those of other companies. Business models can be better differentiated by considering the different levels of IoT architecture, which is also an important implication for further research.

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