Muscle Strength Weighting Based on Deep Learning and Wavelet Packet and Muscle Fatigue Analysis Based on L-Z Complexity
By studying the muscle sound signal of biceps brachii and gastrocnemius muscle, we try to find out the relationship between muscle force and load and the characteristic parameters of fatigue stage, so as to guide the exercise training well, ten healthy male college students were selected to perform...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
|
| Series: | Advances in Multimedia |
| Online Access: | http://dx.doi.org/10.1155/2022/1861890 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832565332361871360 |
|---|---|
| author | Zhizhong Liu |
| author_facet | Zhizhong Liu |
| author_sort | Zhizhong Liu |
| collection | DOAJ |
| description | By studying the muscle sound signal of biceps brachii and gastrocnemius muscle, we try to find out the relationship between muscle force and load and the characteristic parameters of fatigue stage, so as to guide the exercise training well, ten healthy male college students were selected to perform static contraction experiments under different loads (0 lbs, 10 lbs....maximum load), and weight-bearing heel-lifting fatigue experiment. The relationship between load and muscle strength was analyzed by wavelet packet weighting and the L-Z complexity was used to analyze the muscle acoustic signal in the fatigue process. It has been verified that the L-Z complexity of the gastrocnemius muscle acoustic signal gradually decreases from the maximum in the early stage, relatively stable in the middle stage, and decreases again in the later stage of the weight-bearing heel-lifting exercise. The wavelet packet weighting algorithm makes the muscle strength and the weight-bearing well in line with the linear relationship, and the application of muscle strength map can better reflect the load of muscle. The L-Z complexity reflects the changes in muscle fiber recruitment during muscle fatigue and contraction to a certain extent, and provides a scientific basis for judging the fatigue state. |
| format | Article |
| id | doaj-art-cf6c3ce9f8d84244a02ae43dfd6ad3fa |
| institution | Kabale University |
| issn | 1687-5699 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Multimedia |
| spelling | doaj-art-cf6c3ce9f8d84244a02ae43dfd6ad3fa2025-02-03T01:08:45ZengWileyAdvances in Multimedia1687-56992022-01-01202210.1155/2022/1861890Muscle Strength Weighting Based on Deep Learning and Wavelet Packet and Muscle Fatigue Analysis Based on L-Z ComplexityZhizhong Liu0Anhui Polytechnic of Industry & TradeBy studying the muscle sound signal of biceps brachii and gastrocnemius muscle, we try to find out the relationship between muscle force and load and the characteristic parameters of fatigue stage, so as to guide the exercise training well, ten healthy male college students were selected to perform static contraction experiments under different loads (0 lbs, 10 lbs....maximum load), and weight-bearing heel-lifting fatigue experiment. The relationship between load and muscle strength was analyzed by wavelet packet weighting and the L-Z complexity was used to analyze the muscle acoustic signal in the fatigue process. It has been verified that the L-Z complexity of the gastrocnemius muscle acoustic signal gradually decreases from the maximum in the early stage, relatively stable in the middle stage, and decreases again in the later stage of the weight-bearing heel-lifting exercise. The wavelet packet weighting algorithm makes the muscle strength and the weight-bearing well in line with the linear relationship, and the application of muscle strength map can better reflect the load of muscle. The L-Z complexity reflects the changes in muscle fiber recruitment during muscle fatigue and contraction to a certain extent, and provides a scientific basis for judging the fatigue state.http://dx.doi.org/10.1155/2022/1861890 |
| spellingShingle | Zhizhong Liu Muscle Strength Weighting Based on Deep Learning and Wavelet Packet and Muscle Fatigue Analysis Based on L-Z Complexity Advances in Multimedia |
| title | Muscle Strength Weighting Based on Deep Learning and Wavelet Packet and Muscle Fatigue Analysis Based on L-Z Complexity |
| title_full | Muscle Strength Weighting Based on Deep Learning and Wavelet Packet and Muscle Fatigue Analysis Based on L-Z Complexity |
| title_fullStr | Muscle Strength Weighting Based on Deep Learning and Wavelet Packet and Muscle Fatigue Analysis Based on L-Z Complexity |
| title_full_unstemmed | Muscle Strength Weighting Based on Deep Learning and Wavelet Packet and Muscle Fatigue Analysis Based on L-Z Complexity |
| title_short | Muscle Strength Weighting Based on Deep Learning and Wavelet Packet and Muscle Fatigue Analysis Based on L-Z Complexity |
| title_sort | muscle strength weighting based on deep learning and wavelet packet and muscle fatigue analysis based on l z complexity |
| url | http://dx.doi.org/10.1155/2022/1861890 |
| work_keys_str_mv | AT zhizhongliu musclestrengthweightingbasedondeeplearningandwaveletpacketandmusclefatigueanalysisbasedonlzcomplexity |