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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Optimizing Clinical Assessments in Parkinson's Disease Through the Use of Wearable Sensors and Data Driven Modeling
|
|---|---|
| Published in |
Frontiers in Computational Neuroscience, September 2018
|
| DOI | 10.3389/fncom.2018.00072 |
| Pubmed ID | |
| Authors |
Ritesh A. Ramdhani, Anahita Khojandi, Oleg Shylo, Brian H. Kopell |
| Abstract |
The emergence of motion sensors as a tool that provides objective motor performance data on individuals afflicted with Parkinson's disease offers an opportunity to expand the horizon of clinical care for this neurodegenerative condition. Subjective clinical scales and patient based motor diaries have limited clinometric properties and produce a glimpse rather than continuous real time perspective into motor disability. Furthermore, the expansion of machine learn algorithms is yielding novel classification and probabilistic clinical models that stand to change existing treatment paradigms, refine the application of advance therapeutics, and may facilitate the development and testing of disease modifying agents for this disease. We review the use of inertial sensors and machine learning algorithms in Parkinson's disease. |
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 % |
|---|---|---|
| Czechia | 1 | 33% |
| Switzerland | 1 | 33% |
| United States | 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 109 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 109 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 20 | 18% |
| Researcher | 11 | 10% |
| Student > Master | 9 | 8% |
| Student > Bachelor | 8 | 7% |
| Other | 6 | 6% |
| Other | 19 | 17% |
| Unknown | 36 | 33% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 22 | 20% |
| Computer Science | 12 | 11% |
| Neuroscience | 11 | 10% |
| Nursing and Health Professions | 4 | 4% |
| Medicine and Dentistry | 4 | 4% |
| Other | 18 | 17% |
| Unknown | 38 | 35% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 09 January 2019.
All research outputs
#15,545,423
of 23,103,436 outputs
Outputs from Frontiers in Computational Neuroscience
#874
of 1,358 outputs
Outputs of similar age
#213,372
of 337,559 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#21
of 29 outputs
Altmetric has tracked 23,103,436 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,358 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.1. This one is in the 29th percentile – i.e., 29% of its peers scored the same or lower than it.
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 337,559 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 29 others from the same source and published within six weeks on either side of this one. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.