scholarly journals Load-cell Based Smart Floor Panel for Indoor Localization Applications

Author(s):  
Chung-Hao Cheng ◽  
Ming-Feng Wu ◽  
Chin-Tien Huang ◽  
Kuo-Shen Chen

2019 ◽  
Vol 285 ◽  
pp. 468-481 ◽  
Author(s):  
Chung-Hao Cheng ◽  
Ming-Feng Wu ◽  
Shu-Heng Guo ◽  
Kuo-Shen Chen


Author(s):  
Nadia Ghariani ◽  
Mohamed Salah Karoui ◽  
Mondher Chaoui ◽  
Mongi Lahiani ◽  
Hamadi Ghariani


Author(s):  
Mohammad Salimibeni ◽  
Zohreh Hajiakhondi-Meybodi ◽  
Parvin Malekzadeh ◽  
Mohammadamin Atashi ◽  
Konstantinos N. Plataniotis ◽  
...  


CHIPSET ◽  
2020 ◽  
Vol 1 (02) ◽  
pp. 61-68
Author(s):  
Anisha Fadia Haya ◽  
Werman kasoep ◽  
Nefy Puteri Novani

This study aims to create a system that can monitor gas cylinders where this device consists of two systems, the first is a system to measure the weight of 3kg LPG gas cylinders to find the remaining gas which will then be displayed on the LCD, and the second the system gives a notification (alarm) if there is a gas leak via SMS. This system consists of Arduino UNO Microcontroller components, Load cell Sensor, MQ-6 Sensor, and SIM800L GSM Module. For overall system testing, the load cell sensor system can display a percentage of the weight value obtained an error rate of 0%, this indicates that the formula used in the program runs according to what is desired. In the MQ-6 sensor system can make the buzzer on at a value >= 700 ppm, the results of the buzzer can live when the detected gas value >= 700 ppm, this is as desired. In the sim800L gsm module system can send leak notifications, the results obtained that the module can send SMS notifications. And the system turns on the buzzer when the LPG gas has reached the minimum limit, the results obtained by the buzzer will sound when the remaining gas value <= 16%. Based on tests conducted on this system the system can measure the desired weight of the cylinder to look for the remaining gas in the form of a percentage and detect a gas leak and then send an SMS notification.



Barometer ◽  
2020 ◽  
Vol 5 (2) ◽  
pp. 267-271
Author(s):  
Martinus Martinus ◽  
Mareli Telaumbanua ◽  
Meizano Ardi Muhammad ◽  
Adi Susilo
Keyword(s):  

Kebutuhan manusia semakin lama semakin meningkat, berkembang dan bervariasi, untuk memenuhi kebutuhan tersebut industri membutuhkan suatu alat yang dapat mengontrol dan mengendalikan proses permesinan secara otomatis. Diantaranya industri yang memerlukan pengendalian kualitas secara otomatis adalah industri makanan dan minuman instan. Di Indonesia, industri minuman didominasi oleh hasil olahan minuman instan bubuk dan cair. Salah satunya minuman instan kopi, minuman kopi membutuhkan beberapa tahap pengolahan, salah satu yang terpenting adalah proses pemutuan biji kopi. Saat ini industri masih menggunakan tenaga konvensional yang memakan waktu, biaya, tenaga operator. Penentuan mutu dengan cara seperti ini mempunyai kelemahan dari sisi subyektivitas yang memungkinkan terjadinya kesalahan akibat kelelahan mata manusia terhadap contoh yang dianalisis. Untuk menanggulangi masalah tersebut perlu adanya mesin pemutuan biji kopi dengan sistem otomasi berdasarkan parameter besar dan kecilnya biji kopi. Dengan cara menghitung banyaknya biji kopi pada sampel 300 gram biji kopi. Penyelesaian rancang bangun ini dilakukan dengan 2 tahapan yaitu perancangan konveyor sabuk dan perancangan otomasi. Perancangan konveyor sabuk pemilah terdiri dari pemilihan konsep, desain rinci menggunakan aplikasi Solidwork dan proses pembuatan berdasarkan desain. Selanjutnya, perancangan otomasi menggunakan mikrokontroller Arduino Uno, sensor load cell dan sensor FC-51. Setelah peralatan sudah dipasang semua selanjutnya dilakukan pengujian didapatkan dari 300 gram biji kopi terhitung jumlah biji kopi yang bervariasi yaitu 907, 954, 976, 1007, dan 1036 biji kopi. Kemudian, ketinggian sensor yang optimal 6 cm dari permukaan belt conveyor dan kecepatan conveyor 1,52 m/menit agar pembacaan sensor FC-51 akurat.



2020 ◽  
Author(s):  
Wenhao Zhang ◽  
Ramin Ramezani ◽  
Zhuoer Xie ◽  
John Shen ◽  
David Elashoff ◽  
...  

BACKGROUND The availability of low cost ubiquitous wearable sensors has enabled researchers, in recent years, to collect a large volume of data in various domains including healthcare. The goal has been to harness wearables to further investigate human activity, physiology and functional patterns. As such, on-body sensors have been primarily used in healthcare domain to help predict adverse outcomes such as hospitalizations or fall, thereby enabling clinicians to develop better intervention guidelines and personalized models of care to prevent harmful outcomes. In the previous studies [9,10] and the patent application [11], we introduced a generic framework (Sensing At-Risk Population) that draws on the classification of human movements using a 3-axial accelerometer and extraction of indoor localization using BLE beacons, in concert. This work is to address the longitudinal analyses of a particular cohort using the introduced framework in a skilled nursing facility. OBJECTIVE (a) To observe longitudinal changes of physical activity and indoor localization features of rehabilitation-dwelling patients, (b) to assess if such changes can be used at early stages during the rehabilitation period to discriminate between patients that will be re-hospitalized versus the ones that will be discharged to a community setting and (c) to investigate if the sensor based longitudinal changes can imitate patients changes captured by therapist assessments over the course of rehabilitation. METHODS Pearson correlation was used to compare occupational therapy (OT) and physical therapy (PT) assessments with sensor-based features. Generalized Linear Mixed Model was used to find associations between functional measures with sensor based features. RESULTS Energy intensity at therapy room was positively associated with transfer general (β=0.22;SE=0.08;p<.05). Similarly, sitting energy intensity showed positive association with transfer general (β=0.16;SE=0.07;p<.05). Laying down energy intensity was negatively associated with hygiene grooming (β=-0.27;SE=0.14;p<.05). The interaction of sitting energy intensity with time (β=-0.13;SE=.06;p<.05) was associated with toileting general. Dressing lower body was strongly correlated with overall energy intensity (r = 0.66), standing energy intensity (r = 0.61), and laying down energy intensity (r = 0.72) on the first clinical assessment session. CONCLUSIONS This study demonstrates that a combination of indoor localization and physical activity tracking produces a series of features, a subset of which can provide crucial information on the storyline of daily and longitudinal activity patterns of rehabilitation-dwelling patients.



IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 57036-57048 ◽  
Author(s):  
Xinxin Wang ◽  
Danyang Qin ◽  
Ruolin Guo ◽  
Min Zhao ◽  
Lin Ma ◽  
...  


IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 59750-59759
Author(s):  
Ahmed M. Elmoogy ◽  
Xiaodai Dong ◽  
Tao Lu ◽  
Robert Westendorp ◽  
Kishore Reddy Tarimala


2013 ◽  
Vol 17 (1) ◽  
pp. 133-138 ◽  
Author(s):  
Ehsan Ahmed ◽  
Wan Hamidon Wan Badaruzzaman


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