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Review and Preview

In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
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Related Experiment Video

Updated: Jan 27, 2026

Visualizing Motion Patterns in Acupuncture Manipulation

Visualizing Motion Patterns in Acupuncture Manipulation

Published on: July 16, 2016

Predicting and Early Detection of Delirium through Motion Patterns: A Narrative Review.

Ji Sun Hong¹, Na Yeon Kim¹, Hye Ri Kim¹

¹Department of Psychiatry, College of Medicine, Chung-Ang University, Seoul, Korea.

Clinical Psychopharmacology and Neuroscience : the Official Scientific Journal of the Korean College of Neuropsychopharmacology

|January 26, 2026

Summary

Wearable sensors can detect delirium by monitoring sleep-wake cycles and activity patterns. This technology shows promise for early detection and risk stratification in various patient groups.

Keywords:

Actigraphy Circadian rhythm Delirium Machine learning Motor activity Wearable electronic devices

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Last Updated: Jan 27, 2026

Visualizing Motion Patterns in Acupuncture Manipulation

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Published on: October 25, 2024

Area of Science:

Neuroscience
Gerontology
Medical Technology

Background:

Delirium is a frequent acute neuropsychiatric condition.
Early detection of delirium can enhance patient outcomes.
Wearable sensors offer a novel approach to monitoring patient status.

Purpose of the Study:

To review the use of wearable sensors (actigraphy) for delirium prediction and detection.
To synthesize evidence on motion patterns associated with delirium.
To evaluate the potential of continuous motion monitoring for clinical application.

Main Methods:

Narrative review of 11 original studies and 2 systematic reviews.
Analysis of data from wearable sensors (actigraphy) in surgical, ICU, and geriatric populations.
Examination of rest-activity rhythms and motor patterns related to delirium subtypes.

Main Results:

Delirium is linked to disrupted rest-activity rhythms (e.g., altered daytime/nighttime activity, fragmented sleep).
Wrist accelerometry can objectively detect delirium onset and motor subtypes (hyperactive vs. hypoactive).
Machine learning models incorporating motion features improved delirium prediction accuracy from 62% to 74%.

Conclusions:

Continuous motion monitoring via wearable sensors is a feasible, non-invasive tool for early delirium detection.
Motion-based algorithms show moderate sensitivity and require further validation.
Integration with clinical risk factors is necessary for widespread clinical implementation.