ReviewBiosensors2022
Progress in Data Acquisition of Wearable Sensors.
Review in Biosensors, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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.
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.
Who cites it
8 citing papers in PubMed.
- Polymer-Based Flexible Wireless Sensors for Health Monitoring.Nano-micro letters · 2026Review
- Advances in fully integrated wearable near-infrared spectroscopy: hardware innovations and biomedical applications.JPhys photonics · 2026Review
- Emergency Clinical Decision for Sports Injury Management: A Wearable Sensor-Driven Framework from Training to Rehabilitation.Biosensors · 2026Review
- Challenges of Wearable Biosensors and Ways to Overcome Them.Biosensors · 2026Review
- Uncertainty-aware federated temporal learning with explainable LLM-based coaching for privacy-preserving wearable health systems.Frontiers in artificial intelligence · 2026Article
- Non-Intrusive Monitoring and Detection of Mobility Loss in Older Adults Using Binary Sensors.Sensors (Basel, Switzerland) · 2025Article
- A Sensor-Based Classification for Neuromotor Robot-Assisted Rehabilitation.Bioengineering (Basel, Switzerland) · 2025Review
- Wearable Sensor Data Classification for Identifying Missing Transmission Sequence Using Tree Learning.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Wearable sensors have demonstrated wide applications from medical treatment, health monitoring to real-time tracking, human-machine interface, smart home, and motion capture because of the capability of in situ and online monitoring. Data acquisition is extremely important for wearable sensors, including modules of probes, signal conditioning, and analog-to-digital conversion. However, signal conditioning, analog-to-digital conversion, and data transmission have received less attention than probes, especially flexible sensing materials, in research on wearable sensors. Here, as a supplement, this paper systematically reviews the recent progress of characteristics, applications, and optimizations of transistor amplifiers and typical filters in signal conditioning, and mainstream analog-to-digital conversion strategies. Moreover, possible research directions on the data acquisition of wearable sensors are discussed at the end of the paper.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.