An Efficient Data Compression Algorithm For Real-Time Monitoring Applications In Healthcare

Latif, Jawwad; Mehryar, Pouyan; Hou, Lei; Ali, Zulfiqur

doi:10.1109/icccs49078.2020.9118600

Cited by 7 publications

(7 citation statements)

References 16 publications

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“…In the paper, we demonstrated that the proposed use of the O-MAS-R compression algorithm maintained a greater compression ratio than the MAS algorithm at a 53% reduction in data transmission power consumption [20]. As the compression ratio is directly proportional to data transmission power usage, implementing the O-MAS-R algorithm in wireless sensor network sensor nodes will result in even lower data transmission power consumption [21]. This approach uses the least amount of memory to store and transmit data by reducing consecutively repeated data values.…”

Section: Discussionmentioning

confidence: 93%

“…Similarly, the encoding function of the MAS and O-MAS-R algorithms in compressing Accelerometer dataset values took 18.90us and 19.25 us, respectively. However, due to high frequency of repetitions of data in accelerometer dataset, the total run time for O_MAS_R encoding algorithm is signi cantly reduced from 283.53 ms to 71.67 ms [21].…”

Section: B Data Compression Algorithmmentioning

confidence: 99%

“…One of the signi cant limitations for the autonomous telehealth wheelchair is the battery life. The operating of biophysical sensors embedded in the wheelchair is limited by various resources, such as power supply, memory storage and processing capabilities [20] [21]. Continuous monitoring sensors produce a large amount of data and consume signi cant storage memory and transmission power.…”

Section: Introductionmentioning

confidence: 99%

“…However, S-LZW is a dictionary-based algorithm that occupies memory for calculation, so it is not suitable for sensors with restricted RAM [21]. Another compression algorithm of Run Length Encoding (RLE) works by removing duplicate data values.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An Autonomous Wheelchair with Health Monitoring System based on Internet of Thing

Hou,

Latif,

Mehryar

et al. 2024

Preprint

Self Cite

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Assistive powered wheelchairs will bring patients and elderly the ability of remain mobile without the direct intervention from caregivers. Vital signs from users can be collected and analyzed remotely to allow better disease prevention and proactive management of health and chronic conditions. This research proposes an autonomous wheelchair prototype system integrated with biophysical sensors based on internet of thing (IoT). A powered wheelchair system was developed with three biophysical sensors to collect, transmit and analysis users’ four vital signs to provide real-time feedback to users and clinicians. A user interface software embedded with the cloud artificial intelligence (AI) algorithms was developed for the data visualization and analysis. An improved data compression algorithm Minimalist, Adaptive and Streaming R-bit (O-MAS-R) was proposed to achieve a higher compression ratio with minimum 7.1%, maximum 45.25% compared with MAS algorithm during the data transmission. At the same time, the prototype wheelchair, accompanied with a smart-chair app, assimilates data from the onboard sensors and characteristics features within the surroundings in real-time to achieve the functions including obstruct laser scanning, autonomous localization, and point-to-point route planning and moving within a predefined area. In conclusion, the wheelchair prototype uses AI algorithms and navigation technology to help patients and elderly maintain their independent mobility and monitor their healthcare information in real-time.

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Section: Discussionmentioning

confidence: 93%

Section: B Data Compression Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Autonomous Wheelchair with Health Monitoring System based on Internet of Thing

Hou,

Latif,

Mehryar

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…This may appear like magic, but does, in fact, work well for some data types. Data compression is used multimedia formats for images, Video and audio [1,2]. The lossless data compression indicates that data is the same at the source and destination [3,4].…”

Section: Introductionmentioning

confidence: 99%

FPGA implementation of Lempel-Ziv data compression

Mostafa

Zekry

Zakaria

2021

IJRES

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When transmitting the data in digital communication, it is well desired that the transmitting data bits should be as minimal as possible, so many techniques are used to compress the data. In this paper, a Lempel-Ziv algorithm for data compression was implemented through VHDL coding. One of the most lossless data compression algorithms commonly used is Lempel-Ziv. The work in this paper is devoted to improve the compression rate, space-saving, and utilization of the Lempel-Ziv algorithm using a systolic array approach. The developed design is validated with VHDL simulations using Xilinx ISE 14.5 and synthesized on Virtex-6 FPGA chip. The results show that our design is efficient in providing high compression rates and space-saving percentage as well as improved utilization. The Throughput is increased by 50% and the design area is decreased by more than 23% with a high compression ratio compared to comparable previous designs.

show abstract

An autonomous wheelchair with health monitoring system based on Internet of Thing

Hou,

Latif,

Mehryar

et al. 2024

Sci Rep

Self Cite

View full text Add to dashboard Cite

Assistive powered wheelchairs will bring patients and elderly the ability of remain mobile without the direct intervention from caregivers. Vital signs from users can be collected and analyzed remotely to allow better disease prevention and proactive management of health and chronic conditions. This research proposes an autonomous wheelchair prototype system integrated with biophysical sensors based on Internet of Thing (IoT). A powered wheelchair system was developed with three biophysical sensors to collect, transmit and analysis users’ four vital signs to provide real-time feedback to users and clinicians. A user interface software embedded with the cloud artificial intelligence (AI) algorithms was developed for the data visualization and analysis. An improved data compression algorithm Minimalist, Adaptive and Streaming R-bit (O-MAS-R) was proposed to achieve a higher compression ratio with minimum 7.1%, maximum 45.25% compared with MAS algorithm during the data transmission. At the same time, the prototype wheelchair, accompanied with a smart-chair app, assimilates data from the onboard sensors and characteristics features within the surroundings in real-time to achieve the functions including obstruct laser scanning, autonomous localization, and point-to-point route planning and moving within a predefined area. In conclusion, the wheelchair prototype uses AI algorithms and navigation technology to help patients and elderly maintain their independent mobility and monitor their healthcare information in real-time.

show abstract

An Efficient Data Compression Algorithm For Real-Time Monitoring Applications In Healthcare

Cited by 7 publications

References 16 publications

An Autonomous Wheelchair with Health Monitoring System based on Internet of Thing

An Autonomous Wheelchair with Health Monitoring System based on Internet of Thing

FPGA implementation of Lempel-Ziv data compression

An autonomous wheelchair with health monitoring system based on Internet of Thing

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