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Optimization of Forcemyography Sensor Placement for Arm Movement Recognition
Computer Vision and Pattern Recognition · Submitted 22 Jul 2022
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Our experiments show that using only 4 sensors optimized with our algorithm can help maintain a comparable recognition accuracy to using all sensors.
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The abstract, sentence by sentence
How to design an optimal wearable device for human movement recognition is vital to reliable and accurate human-machine collaboration. Previous works mainly fabricate wearable devices heuristically. Instead, this paper raises an academic question: can we design an optimization algorithm to optimize the fabrication of wearable devices such as figuring out the best sensor arrangement automatically? Specifically, this work focuses on optimizing the placement of Forcemyography (FMG) sensors for FMG armbands in the application of arm movement recognition. Firstly, based on graph theory, the armband is modeled considering sensors' signals and connectivity. Then, a Graph-based Armband Modeling Network (GAM-Net) is introduced for arm movement recognition. Afterward, the sensor placement optimization for FMG armbands is formulated and an optimization algorithm with greedy local search is proposed. To study the effectiveness of our optimization algorithm, a dataset for mechanical maintenance tasks using FMG armbands with 16 sensors is collected. Our experiments show that using only 4 sensors optimized with our algorithm can help maintain a comparable recognition accuracy to using all sensors. Finally, the optimized sensor placement result is verified from a physiological view. This work would like to shed light on the automatic fabrication of wearable devices considering downstream tasks, such as human biological signal collection and movement recognition. Our code and dataset are available at https://github.com/JerryX1110/IROS22-FMG-Sensor-Optimization
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | How to design an optimal wearable device for human movement recognition is vital to reliable and accurate human-machine collaboration. | background 1.00 | 0.05 | 0.02 | asserted 1.00 |
| s1 | Previous works mainly fabricate wearable devices heuristically. | background 0.68 | 0.03 | 0.01 | asserted 1.00 |
| s2 | Instead, this paper raises an academic question: can we design an optimization algorithm to optimize the fabrication of wearable devices such as figuring out the best sensor arrangement automatically? | gap 0.61 | 0.39 | 0.03 | hedged possibility 0.54 |
| s3 | Specifically, this work focuses on optimizing the placement of Forcemyography (FMG) sensors for FMG armbands in the application of arm movement recognition. | contribution 0.94 | 0.52 | 0.03 | asserted 0.92 |
| s4 | Firstly, based on graph theory, the armband is modeled considering sensors' signals and connectivity. | method 0.99 | 0.91 | 0.02 | asserted 0.99 |
| s5 | Then, a Graph-based Armband Modeling Network (GAM-Net) is introduced for arm movement recognition. | method 0.87 | 0.90 | 0.03 | asserted 0.99 |
| s6 | Afterward, the sensor placement optimization for FMG armbands is formulated and an optimization algorithm with greedy local search is proposed. | method 0.84 | 0.95 | 0.05 | asserted 0.99 |
| s7 | To study the effectiveness of our optimization algorithm, a dataset for mechanical maintenance tasks using FMG armbands with 16 sensors is collected. | method 0.96 | 0.96 | 0.03 | asserted 0.99 |
| s8 | Our experiments show that using only 4 sensors optimized with our algorithm can help maintain a comparable recognition accuracy to using all sensors. | result 1.00 | 0.98 | 0.76 | asserted 0.92 |
| s9 | Finally, the optimized sensor placement result is verified from a physiological view. | result 0.33 | 0.87 | 0.06 | asserted 0.99 |
| s10 | This work would like to shed light on the automatic fabrication of wearable devices considering downstream tasks, such as human biological signal collection and movement recognition. | interpretation 0.49 | 0.22 | 0.09 | hedged possibility 0.76 |
| s11 | Our code and dataset are available at https://github.com/JerryX1110/IROS22-FMG-Sensor-Optimization | contribution 0.68 | 0.62 | 0.03 | asserted 0.98 |
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Original abstract
How to design an optimal wearable device for human movement recognition is vital to reliable and accurate human-machine collaboration. Previous works mainly fabricate wearable devices heuristically. Instead, this paper raises an academic question: can we design an optimization algorithm to optimize the fabrication of wearable devices such as figuring out the best sensor arrangement automatically? Specifically, this work focuses on optimizing the placement of Forcemyography (FMG) sensors for FMG armbands in the application of arm movement recognition. Firstly, based on graph theory, the armband is modeled considering sensors' signals and connectivity. Then, a Graph-based Armband Modeling Network (GAM-Net) is introduced for arm movement recognition. Afterward, the sensor placement optimization for FMG armbands is formulated and an optimization algorithm with greedy local search is proposed. To study the effectiveness of our optimization algorithm, a dataset for mechanical maintenance tasks using FMG armbands with 16 sensors is collected. Our experiments show that using only 4 sensors optimized with our algorithm can help maintain a comparable recognition accuracy to using all sensors. Finally, the optimized sensor placement result is verified from a physiological view. This work would like to shed light on the automatic fabrication of wearable devices considering downstream tasks, such as human biological signal collection and movement recognition. Our code and dataset are available at https://github.com/JerryX1110/IROS22-FMG-Sensor-Optimization
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Uses the method or tool of 1 paper
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Based on the generic graph modeling of special-shaped FMG armband considering both FMG signals $𝒳$ the connectivity $𝒜$, as is shown in Fig. [ref], we further leverage graph convolutional network (GCN) [ref] to build our classifier for FMG armbands, namely Graph-based Armband Modeling Network (GAM-Net), for arm movement recognition.
Semi-Supervised Classification with Graph Convolutional Networks · Graph-based Armband Modeling Network for recognition · 0.99
Cites as background 1 paper
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To approach such a lofty goal of human-machine symbiosis, a growing effort has been devoted to designing more powerful wearable sensing devices and smarter algorithms to recognize human movement or intention [ref] , especially in the field of human-machine interaction [ref] [ref] , augmented reality [ref] , and Metaverse [ref] .
All One Needs to Know about Metaverse: A Complete Survey on Technological Singularity, Virtual Ecosystem, and Research Agenda · INTRODUCTION · 0.97
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