scholarly journals Wearable System for Daily Activity Recognition Using Inertial and Pressure Sensors of a Smart Band and Smart Shoes

2019 ◽  
Vol 14 (6) ◽  
pp. 726-742 ◽  
Author(s):  
Phuc Huu Truong ◽  
Sujeong You ◽  
Sang-Hoon Ji ◽  
Gu-Min Jeong
Keyword(s):  
Activity Recognition ◽  
Wearable Sensors ◽  
Pressure Sensors ◽  
Body Part ◽  
Research Approach ◽  
Sensing Technology ◽  
Multimodal Sensors ◽  
Selection For ◽  
Using Data ◽  
Fusion Scheme

Human Activity Recognition (HAR) is a challenging task in the field of human-related signal processing. Owing to the development of wearable sensing technology, an emerging research approach in HAR is to identify user-performed tasks by using data collected from wearable sensors. In this paper, we propose a novel system for monitoring and recognizing daily living activities using an off-the-shelf smart band and two smart shoes. The system aims at providing a useful tool for solving problems regarding body part placement, fusion of multimodal sensors and feature selection for a specific set of activities. The system collects inertial and plantar pressure data at wrist and foot to analyze and then, extract, select important features for recognition. We construct and compare two predictive models of classifying activities from the reduced feature set. A comparison of the classification for each wearable device and a fusion scheme is provided to identify the best body part for activity recognition: either the wrist or the feet. This comparison also demonstrated the effective HAR performance of the proposed system.

Analysis of Impacts Malaysia's Territorial Limitation (Lockdown) Policy on Indonesia-Malaysia Border Area, Sanggau District

2020 ◽  
Vol 11 (1) ◽  
pp. 156-156
Author(s):  
Ira Patriani
Keyword(s):  
Social Issues ◽  
Negative Impact ◽  
Research Approach ◽  
Border Area ◽  
Border Areas ◽  
The Government ◽  
Using Data ◽  
Virus Spreading ◽  
Kinship Ties ◽  
Collection Methods

Border areas, is one of affected area on COVID_19 this present. Many of people cn not go out as usually, adding almost each country has to implement their territorial limitation (lockdown policy) to minimalize this virus spreading. One of Malaysia State, where very close and get direct border with Indonesia. This research took place at Sanggau District, Entikong, Gun Tembawang Village.The research approach used is qualitative, using data collection methods in the form of interviews, observations, and documentation supported by interviews with the theoretical approach to the negative and positive aspects on policy implementation. Research results, The results stated that the lockdown activities of Malaysia which were affected by the corona virus outbreak needed to be carried out in an effort to minimize the spread of the virus outbreak. Although of course it has a negative impact on the country's economic structure, social issues and other sector. In implementing this lockdown, there is a need for cooperation between the government and the community as well as an agreement with neighboring countries in terms of the mobility of residents closest to each other's territory on exemptions in order to realize social welfare and public health without limiting the origin of the state, religion, community and profession. Especially in border areas where mobility and kinship ties have always been closer than in other regions. Keywords: Border area, lockdown policy, covid_19

scholarly journals Ablation Analysis to Select Wearable Sensors for Classifying Standing, Walking, and Running

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 194
Author(s):  
Sarah Gonzalez ◽  
Paul Stegall ◽  
Harvey Edwards ◽  
Leia Stirling ◽  
Ho Chit Siu
Keyword(s):  
Activity Recognition ◽  
Principal Components ◽  
Classification Accuracy ◽  
Wearable Sensors ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Techniques ◽  
Measurement Units ◽  
The Difference

The field of human activity recognition (HAR) often utilizes wearable sensors and machine learning techniques in order to identify the actions of the subject. This paper considers the activity recognition of walking and running while using a support vector machine (SVM) that was trained on principal components derived from wearable sensor data. An ablation analysis is performed in order to select the subset of sensors that yield the highest classification accuracy. The paper also compares principal components across trials to inform the similarity of the trials. Five subjects were instructed to perform standing, walking, running, and sprinting on a self-paced treadmill, and the data were recorded while using surface electromyography sensors (sEMGs), inertial measurement units (IMUs), and force plates. When all of the sensors were included, the SVM had over 90% classification accuracy using only the first three principal components of the data with the classes of stand, walk, and run/sprint (combined run and sprint class). It was found that sensors that were placed only on the lower leg produce higher accuracies than sensors placed on the upper leg. There was a small decrease in accuracy when the force plates are ablated, but the difference may not be operationally relevant. Using only accelerometers without sEMGs was shown to decrease the accuracy of the SVM.

scholarly journals Wearable Sensor-Based Real-Time Gait Detection: A Systematic Review

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2727
Author(s):  
Hari Prasanth ◽  
Miroslav Caban ◽  
Urs Keller ◽  
Grégoire Courtine ◽  
Auke Ijspeert ◽  
...  
Keyword(s):  
Systematic Review ◽  
Gait Analysis ◽  
Real Time ◽  
Wearable Sensors ◽  
Pressure Sensors ◽  
Clinical Applications ◽  
Performance Comparison ◽  
Heel Strike ◽  
Rule Based ◽  
Pathological Gait

Gait analysis has traditionally been carried out in a laboratory environment using expensive equipment, but, recently, reliable, affordable, and wearable sensors have enabled integration into clinical applications as well as use during activities of daily living. Real-time gait analysis is key to the development of gait rehabilitation techniques and assistive devices such as neuroprostheses. This article presents a systematic review of wearable sensors and techniques used in real-time gait analysis, and their application to pathological gait. From four major scientific databases, we identified 1262 articles of which 113 were analyzed in full-text. We found that heel strike and toe off are the most sought-after gait events. Inertial measurement units (IMU) are the most widely used wearable sensors and the shank and foot are the preferred placements. Insole pressure sensors are the most common sensors for ground-truth validation for IMU-based gait detection. Rule-based techniques relying on threshold or peak detection are the most widely used gait detection method. The heterogeneity of evaluation criteria prevented quantitative performance comparison of all methods. Although most studies predicted that the proposed methods would work on pathological gait, less than one third were validated on such data. Clinical applications of gait detection algorithms were considered, and we recommend a combination of IMU and rule-based methods as an optimal solution.

scholarly journals Improving Human Activity Recognition Performance by Data Fusion and Feature Engineering

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 692
Author(s):  
Jingcheng Chen ◽  
Yining Sun ◽  
Shaoming Sun
Keyword(s):  
Feature Selection ◽  
Activity Recognition ◽  
Human Activity ◽  
Joint Angle ◽  
Recognition Performance ◽  
Wearable Sensors ◽  
Estimation Algorithm ◽  
Recognition System ◽  
Human Activity Recognition ◽  
Time Frequency

Human activity recognition (HAR) is essential in many health-related fields. A variety of technologies based on different sensors have been developed for HAR. Among them, fusion from heterogeneous wearable sensors has been developed as it is portable, non-interventional and accurate for HAR. To be applied in real-time use with limited resources, the activity recognition system must be compact and reliable. This requirement can be achieved by feature selection (FS). By eliminating irrelevant and redundant features, the system burden is reduced with good classification performance (CP). This manuscript proposes a two-stage genetic algorithm-based feature selection algorithm with a fixed activation number (GFSFAN), which is implemented on the datasets with a variety of time, frequency and time-frequency domain features extracted from the collected raw time series of nine activities of daily living (ADL). Six classifiers are used to evaluate the effects of selected feature subsets from different FS algorithms on HAR performance. The results indicate that GFSFAN can achieve good CP with a small size. A sensor-to-segment coordinate calibration algorithm and lower-limb joint angle estimation algorithm are introduced. Experiments on the effect of the calibration and the introduction of joint angle on HAR shows that both of them can improve the CP.

Constructing the Seismograms of Future Earthquakes in Yunnan, China, Using Compressed Sensing

2020 ◽  
Vol 92 (1) ◽  
pp. 261-274
Author(s):  
Jie Zhang ◽  
Huiyu Zhu ◽  
Siwei Yu ◽  
Jianwei Ma
Keyword(s):  
Compressed Sensing ◽  
Active Fault ◽  
Regional Scale ◽  
Focal Mechanism Solution ◽  
Total Output ◽  
Detailed Knowledge ◽  
Large Dataset ◽  
Sensing Technology ◽  
Data Driven Approach ◽  
Using Data

Abstract The ability to calculate the seismogram of an earthquake at a local or regional scale is critical but challenging for many seismological studies because detailed knowledge about the 3D heterogeneities in the Earth’s subsurface, although essential, is often insufficient. Here, we present an application of compressed sensing technology that can help predict the seismograms of earthquakes at any position using data from past events randomly distributed in the same area in Jinggu County, Yunnan, China. This first data-driven approach for calculating seismograms generates a large dataset in 3D with a volume encompassing an active fault zone. The input number of earthquakes comprises only 1.27% of the total output events. We use the output data to create a database intended to find the best-matching waveform of a new event by applying an earthquake search engine, which instantly reveals the hypocenter and focal-mechanism solution.

Evolution of top management’s understanding of product quality in Eastern Europe since the end of communism

2017 ◽  
Vol 29 (1) ◽  
pp. 82-100
Author(s):  
Svetoslav Georgiev ◽  
Emil Georgiev
Keyword(s):  
Eastern Europe ◽  
Product Quality ◽  
Quantitative Research ◽  
Business Performance ◽  
Research Approach ◽  
Senior Managers ◽  
Content Type ◽  
Doing Business ◽  
Using Data ◽  
The Impact

Purpose The purpose of this paper is to analyse the evolution of top management’s understanding of product quality in Bulgaria since the end of communism. The study examines three specific areas: top management’s understanding of the term “quality”; top management’s understanding of the relationship between quality and business performance; and top management’s understanding of the impact of job position on quality. Design/methodology/approach The paper relies on a quantitative research approach by using data from a survey of 186 companies in Bulgaria. Findings The paper suggests that senior managers in Bulgaria continue to base their understanding of “quality” on a single approach (*a characteristic of the communist era), with the product-based and the user-based approaches currently being the two most common ones. At the same time, surprisingly enough, this study claims that senior management in Bulgaria is currently well aware of the importance of quality as a dimension of firm’s competitiveness, and is also highly conscious of its roles’ impact on product quality. Research limitations/implications The results of this study are exclusively based on the case of Bulgaria and must be treated with caution in the case of other former communist states from the Central and Eastern Europe (CEE) region. Practical implications This paper has relevance for both managers and companies doing business in Eastern Europe. Originality/value This is the first paper to provide detailed analysis of the evolution of the understanding of “product quality” in CEE since the end of communism. Moreover, this paper applies, for the first time, Garvin’s five approaches to defining quality within a practical context.

Enhanced Sensing and Activity Recognition System Using IoT for Healthcare

2021 ◽  
Vol 13 (2) ◽  
pp. 42-49
Author(s):  
Yamini G. ◽  
Gopinath Ganapathy
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Healthcare Services ◽  
Recognition System ◽  
Physical Activities ◽  
Video Data ◽  
Large Coupling ◽  
Sensing Technology ◽  
Medical Benefits ◽  
Primary Focus

Through the integration of advanced algorithms and smart sensing technology in healthcare services, huge medical benefits could be gained by the aged and sick people in determining their activity recognition. Human activity recognition (HAR) is still in the research for the past decades that promotes recognition of physical activities automatically. The main aim of HAR is to obtain and analyze the physical activities of a person, which could be promoted through several in-built sensors examined in the form of video data. Through this technique, necessary information could be obtained that also helps in preventing significant risks and also averts or alerts unfortunate events from happening. However, there is no particular categorization for human activity, and there is no description of the particular events to occur. The objective of this paper is to propose a healthcare information system based on IoT where enhancing activity recognition is the primary focus. Human activities are supposed to be diverse; it is necessary to choose appropriate sensors and the effective placement of those sensors in recognizing specific activities. One of the major challenges here is choosing the appropriate sensor for that particular instance and gathering data under particular circumstances. Due to the large coupling of sensors and their activity monitoring functionality, the solution to promote feasibility for the HAR predicament cannot be determined. A distinguishing feature of this paper is that it includes future users' perspectives.

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