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Mendeley readers
Attention Score in Context
| Title |
Leg Force Control Through Biarticular Muscles for Human Walking Assistance
|
|---|---|
| Published in |
Frontiers in Neurorobotics, July 2018
|
| DOI | 10.3389/fnbot.2018.00039 |
| Pubmed ID | |
| Authors |
Maziar A. Sharbafi, Hamid Barazesh, Majid Iranikhah, Andre Seyfarth |
| Abstract |
Assistive devices can be considered as one of the main applications of legged locomotion research in daily life. In order to develop an efficient and comfortable prosthesis or exoskeleton, biomechanical studies on human locomotion are very useful. In this paper, the applicability of the FMCH (force modulated compliant hip) model is investigated for control of lower limb wearable exoskeletons. This is a bioinspired method for posture control, which is based on the virtual pivot point (VPP) concept, found in human walking. By implementing the proposed method on a detailed neuromuscular model of human walking, we showed that using a biarticular actuator parallel to the hamstring muscle, activation in most of the leg muscles can be reduced. In addition, the total metabolic cost of motion is decreased up to 12%. The simple control rule of assistance is based on leg force feedback which is the only required sensory information. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Switzerland | 1 | 33% |
| United States | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 52 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 10 | 19% |
| Student > Ph. D. Student | 9 | 17% |
| Researcher | 6 | 12% |
| Student > Doctoral Student | 3 | 6% |
| Lecturer > Senior Lecturer | 3 | 6% |
| Other | 5 | 10% |
| Unknown | 16 | 31% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 27 | 52% |
| Sports and Recreations | 5 | 10% |
| Computer Science | 2 | 4% |
| Medicine and Dentistry | 1 | 2% |
| Unknown | 17 | 33% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 January 2022.
All research outputs
#14,255,539
of 22,858,915 outputs
Outputs from Frontiers in Neurorobotics
#348
of 863 outputs
Outputs of similar age
#184,399
of 325,964 outputs
Outputs of similar age from Frontiers in Neurorobotics
#13
of 25 outputs
Altmetric has tracked 22,858,915 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 863 research outputs from this source. They receive a mean Attention Score of 4.2. This one has gotten more attention than average, scoring higher than 56% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 325,964 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 25 others from the same source and published within six weeks on either side of this one. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.