Monitoring and alerting system to determine muscle strength for fall risk assessment
This report demonstrates the design of a healthcare monitoring and alerting system intended for the elderly using Electromyography (EMG) signals. The EMG signals are captured using a muscle sensor (Myoware muscle sensor) sampled using a microcontroller (Adafruit Feather HUZZAH) and relayed to a m...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2021
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/149079 |
| _version_ | 1826116144413540352 |
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| author | Neo, Darren |
| author2 | Yvonne Lam Ying Hung |
| author_facet | Yvonne Lam Ying Hung Neo, Darren |
| author_sort | Neo, Darren |
| collection | NTU |
| description | This report demonstrates the design of a healthcare monitoring and alerting system
intended for the elderly using Electromyography (EMG) signals. The EMG signals
are captured using a muscle sensor (Myoware muscle sensor) sampled using a
microcontroller (Adafruit Feather HUZZAH) and relayed to a microcomputer
(Raspberry Pi 4B+).
Further python scripts and algorithms would then be run on the collated data in
order to determine the elderly’s muscle strength in real-time, thereafter, alerting the
elderly and/or their caregiver should their muscle state not deemed suitable for
normal use. 2 prototypes are discussed in this paper. Both prototypes use the MQTT
(Message Queuing Telemetry Transport) protocol to transmit data with the first
using a RDBMS (Relational Database Management System) and the other using a
Raw CSV (Comma-separated Values) file for data storage and processing.
During this study, experiments were conducted to determine the relationship
between muscle activations through real life situations that elderly face, such as
having difficulty changing from a sitting position to a standing position. Research
was also conducted to correlate grip strength with overall muscle usage. The two
muscle groups used to conduct the tests were the Gastrocnemius Medialis and the
Flexor Digitorum Profondus respectively. The results conclude that the prototype is
able to detect muscle sensor anomalies with flexible detection mechanisms where
required. |
| first_indexed | 2024-10-01T04:06:31Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/149079 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:06:31Z |
| publishDate | 2021 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1490792023-07-07T17:20:32Z Monitoring and alerting system to determine muscle strength for fall risk assessment Neo, Darren Yvonne Lam Ying Hung School of Electrical and Electronic Engineering EYHLAM@ntu.edu.sg Engineering::Electrical and electronic engineering::Computer hardware, software and systems This report demonstrates the design of a healthcare monitoring and alerting system intended for the elderly using Electromyography (EMG) signals. The EMG signals are captured using a muscle sensor (Myoware muscle sensor) sampled using a microcontroller (Adafruit Feather HUZZAH) and relayed to a microcomputer (Raspberry Pi 4B+). Further python scripts and algorithms would then be run on the collated data in order to determine the elderly’s muscle strength in real-time, thereafter, alerting the elderly and/or their caregiver should their muscle state not deemed suitable for normal use. 2 prototypes are discussed in this paper. Both prototypes use the MQTT (Message Queuing Telemetry Transport) protocol to transmit data with the first using a RDBMS (Relational Database Management System) and the other using a Raw CSV (Comma-separated Values) file for data storage and processing. During this study, experiments were conducted to determine the relationship between muscle activations through real life situations that elderly face, such as having difficulty changing from a sitting position to a standing position. Research was also conducted to correlate grip strength with overall muscle usage. The two muscle groups used to conduct the tests were the Gastrocnemius Medialis and the Flexor Digitorum Profondus respectively. The results conclude that the prototype is able to detect muscle sensor anomalies with flexible detection mechanisms where required. Bachelor of Engineering (Information Engineering and Media) 2021-05-25T06:10:47Z 2021-05-25T06:10:47Z 2021 Final Year Project (FYP) Neo, D. (2021). Monitoring and alerting system to determine muscle strength for fall risk assessment. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/149079 https://hdl.handle.net/10356/149079 en A2274-201 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering::Computer hardware, software and systems Neo, Darren Monitoring and alerting system to determine muscle strength for fall risk assessment |
| title | Monitoring and alerting system to determine muscle strength for fall risk assessment |
| title_full | Monitoring and alerting system to determine muscle strength for fall risk assessment |
| title_fullStr | Monitoring and alerting system to determine muscle strength for fall risk assessment |
| title_full_unstemmed | Monitoring and alerting system to determine muscle strength for fall risk assessment |
| title_short | Monitoring and alerting system to determine muscle strength for fall risk assessment |
| title_sort | monitoring and alerting system to determine muscle strength for fall risk assessment |
| topic | Engineering::Electrical and electronic engineering::Computer hardware, software and systems |
| url | https://hdl.handle.net/10356/149079 |
| work_keys_str_mv | AT neodarren monitoringandalertingsystemtodeterminemusclestrengthforfallriskassessment |