Evidence map›Paper›PMID 34713038›Full record

ArticleFrontiers in digital health2020

Wearable Based Calibration of Contactless In-home Motion Sensors for Physical Activity Monitoring in Community-Dwelling Older Adults.

Narayan Schütz, Hugo Saner, Angela Botros, Philipp Buluschek, Prabitha Urwyler, René M Müri, Tobias Nef

Open access · goldAbstract read
In one paragraph

Article in Frontiers in digital health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
0.8field-weighted citation impact, top 29% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 1 synthesis or guideline pooled it, 7 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 2 institutions in 2 countries.

Narayan SchützARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
Hugo SanerARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
Angela BotrosARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
Philipp BuluschekDomoSafety S.A., Lausanne, Switzerland.
Prabitha UrwylerARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
René M MüriARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
Tobias NefARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
University of Bern · CHUniversity Hospital of Bern · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Passive infrared motion sensors are commonly used in telemonitoring applications to monitor older community-dwelling adults at risk. One possible use case is quantification of in-home physical activity, a key factor and potential digital biomarker for healthy and independent aging. A major disadvantage of passive infrared sensors is their lack of performance and comparability in physical activity quantification. In this work, we calibrate passive infrared motion sensors for in-home physical activity quantification with simultaneously acquired data from wearable accelerometers and use the data to find a suitable correlation between in-home and out-of-home physical activity. We use data from 20 community-dwelling older adults that were simultaneously provided with wireless passive infrared motion sensors in their homes, and a wearable accelerometer for at least 60 days. We applied multiple calibration algorithms and evaluated results based on several statistical and clinical metrics. We found that using even relatively small amounts of wearable based ground-truth data over 7-14 days, passive infrared based wireless sensor systems can be calibrated to give largely better estimates of older adults' daily physical activity. This increase in performance translates directly to stronger correlations of measured physical activity levels with a variety of age relevant health indicators and outcomes known to be associated with physical activity.

Indexed as

ambient assisted livingolder adultsouting imputationpassive infraredpervasive computingphysical activitysensor calibrationtelemonitoring

Identifiers

PMID34713038
PMCPMC8522020
OpenAlexW3122045979

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.