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Gait Monitoring for Early Neurological Disorder Detection Using Sensors in a Smartphone: Validation and a Case Study of Parkinsonism
Paweeya Raknim, Kun-Chan Lan. Telemedicine Journal and e-Health, 2015-08-24
Topic: Brain imaging, connectivity and assessment
On average, the accuracy of our step length estimation was about 98%. Using a binary classification method-namely, support vector machine-we carried out a case study and showed that it was feasible to identify changes in the walking patterns of a PD patient with an accuracy of 94%. Using 1 year of gait trace data obtained from the users' phones, our work provides a first step to experimentally show the possibility of applying smartphone sensor data to provide early warnings to potential PD patients to encourage them to seek medical assistance and thus help doctors diagnose this disease earlier.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Raknim, P., & Lan, K.-C. (2015). Gait Monitoring for Early Neurological Disorder Detection Using Sensors in a Smartphone: Validation and a Case Study of Parkinsonism. Telemedicine Journal and e-Health. https://doi.org/10.1089/tmj.2015.0005
Vancouver
Raknim P, Lan K.-C. Gait Monitoring for Early Neurological Disorder Detection Using Sensors in a Smartphone: Validation and a Case Study of Parkinsonism. Telemedicine Journal and e-Health. 2015. doi:10.1089/tmj.2015.0005. PMID: 26302109.
BibTeX
@article{raknim2015-26302109,
author = {Raknim, Paweeya and Lan, Kun-Chan},
title = {{Gait Monitoring for Early Neurological Disorder Detection Using Sensors in a Smartphone: Validation and a Case Study of Parkinsonism}},
journal = {Telemedicine Journal and e-Health},
year = {2015},
doi = {10.1089/tmj.2015.0005},
pmid = {26302109},
url = {https://doi.org/10.1089/tmj.2015.0005}
}
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Key findings
- On average, the accuracy of our step length estimation was about 98%.
- Using a binary classification method-namely, support vector machine-we carried out a case study and showed that it was feasible to identify changes in the walking patterns of a PD patient with an accuracy of 94%.
- Using 1 year of gait trace data obtained from the users' phones, our work provides a first step to experimentally show the possibility of applying smartphone sensor data to provide early warnings to potential PD patients to encourage them to seek medical assistance and thus help doctors diagnose this disease earlier.
Study details
- Evidence strengthLow
How this study scored 36/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomized / blinded because the record does not state it. We have no data on journal's citation record, times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multicenter collaboration, so those were left out: it earned 21 of the 58 points we could judge it on, which is 36 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Telemedicine Journal and e-Health) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 1 / 5 | 2015 — within 20 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomized / blinded | 0 / 2 | No randomization or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multicenter collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 26302109) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 21 / 58 | Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 21 of the 58 points we could judge it on is 36 out of 100. |
| Total | 36 / 100 | — |
What each of these 20 components measures, and what this score is not →
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