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Utilizing Machine Learning to Recognize Human Activities for Elderly and Homecare
Alaraj R, Alshammari R.. Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH, 2020-09-01
Topic: Risk, prevention and care in population studies
Introduction Dementia is a progressive disorder associated with age, which is characterized by deterioration of individuals' cognitive functions such as the ability to perform routine tasks. With the increase of human life expectancy, the prevalence of dementia patients will reach 152 million in 2050. Unfortunately, there is no treatment available to cure dementia or alter the course of its progression. However, there is an area of support for patients and caregivers to assist daily living. Technological devices and applications are increasingly advancing, exploiting sensory data for dementia patients and homecare using smartphones to permit monitoring of their activities. Aim This paper uses the labeled dataset besides comparing the 3-classification algorithm to evaluate whether or not the algorithms deployed can classify the activities with high accuracy. Results A public data is used to classify human activities into one of the six activities, BigML platform is used to build machine learning models. Results show that machine learning algorithms can achieve high accuracy. The activity recognition algorithms are highly accurate using ridged regression and deep neural networks, with almost all activities being recognized correctly over 98% of the time. Conclusion An application of smartphones can be utilized for human activities monitoring by proposing a high level for dementia patients and homecare monitoring services. Using this service, the patients only need to carry the smartphone, and their caregivers simply need to use the application that monitors their patients' activities.
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APA 7th
Alaraj, R., & Alshammari, R. (2020). Utilizing Machine Learning to Recognize Human Activities for Elderly and Homecare. Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH. https://doi.org/10.5455/aim.2020.28.196-201
Vancouver
Alaraj R, Alshammari R. Utilizing Machine Learning to Recognize Human Activities for Elderly and Homecare. Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH. 2020. doi:10.5455/aim.2020.28.196-201. PMID: 33417637.
BibTeX
@article{alaraj2020-33417637,
author = {Alaraj, R. and Alshammari, R.},
title = {{Utilizing Machine Learning to Recognize Human Activities for Elderly and Homecare}},
journal = {Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia \& Herzegovina : casopis Drustva za medicinsku informatiku BiH},
year = {2020},
doi = {10.5455/aim.2020.28.196-201},
pmid = {33417637},
url = {https://doi.org/10.5455/aim.2020.28.196-201}
}
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Key findings
- Introduction Dementia is a progressive disorder associated with age, which is characterized by deterioration of individuals' cognitive functions such as the ability to perform routine tasks.
- Results A public data is used to classify human activities into one of the six activities, BigML platform is used to build machine learning models.
- Results show that machine learning algorithms can achieve high accuracy.
Study details
- Evidence strengthLow
How this study scored 41/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / 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 multi-centre collaboration, so those were left out: it earned 24 of the 58 points we could judge it on, which is 41 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH) |
| 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 | 2 / 5 | 2020 — within 10 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) |
| Randomised / blinded | 0 / 2 | No randomisation 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 |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 33417637) |
| Free to read | 2 / 2 | Free to read |
| Points | 24 / 58 | Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 24 of the 58 points we could judge it on is 41 out of 100. |
| Total | 41 / 100 | — |
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Also tagged “activities”
4 studies in the library carry the topic tag “activities”; these are this study's neighbours in a fixed, alphabetical-by-hash tour of them, not the closest in content. All 4 tagged studies.