Using Indoor Movement Complexity in Smart Homes to Detect Frailty in Older Adults - Multiple-Methods Case Series Study

Sep 29, 2026

Complexity theory in aging links the adaptive capacity of older adults to the complexity of sensor-derived physiological and behavioral signals, with frailty reflecting reduced adaptability. Smart homes equipped with ambient sensors enable real-world, longitudinal study of indoor movement complexity. This paper presents a computational approach to estimate behavior complexity from sensor data and applies a multiple-methods case series to examine entropy-based complexity measures and frailty in independent community-dwelling older adults.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages17

Reading Time43 min

AccessFree to download

Background

Frailty as a public health challenge

Aging in place and detection needs

Complexity theory and smart home sensors

Methods

Measuring indoor movement complexity

Study cases and weekly frailty assessment

Entropy-based complexity indices

Results

Longitudinal changes in complexity

Associations with frailty progression

Conclusions

Diversity of complexity trajectories

Role of potential confounders

JMIR AGING Wuestney et al Original Paper Using Indoor Movement Complexity in Smart Homes to Detect Frailty in Older Adults: Multiple-Methods Case Series Study | Katherine Wuestney 1 , PhD; Diane Cook2 , MSci, PhD; Catherine Van Son 1 , MSN, PhD; Roschelle Fritz3 , MSN, PhD | | --- | | 1College of Nursing, Washington State University, Pullman, WA, United States
2School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, United States 3Betty Irene Moore School of Nursing, UC Davis Health System, Sacramento, CA, United States
Corresponding Author:
Roschelle Fritz, MSN, PhD
Betty Irene Moore School of Nursing UC Davis Health System
2570 48th St Sacramento, CA 95817 United States
Phone: 1 916 734 2145
Email: [rlfritz@ucdavis.edu](rlfritz@ucdavis.edu)
Abstract | | Background: The theory of complexity in aging indicates that the complexity of sensor-derived physiological and behavioral signals reflects an older adult’s adaptive capacity and, in turn, their frailty. Smart homes with ambient sensors offer a unique opportunity to longitudinally explore the complexity of older adults’ indoor movement in a real-world setting. Here, we introduce a computational method to estimate behavior complexity from sensor data. We further conduct a multiple-methods case series to explore the relationship between entropy-measured smart home data complexity and older adult frailty.
Objective: This study aims to explore the relationship between entropy-measured ambient sensor data complexity and frailty in independent community-dwelling older adults.
Methods: The nature of older adults’ indoor movement complexity is measured by quantifying the entropy of smart home data. Overall, 11 cases with persons aged 65 years and older were drawn from an ongoing smart home study to illustrate the method. We assessed weekly frailty for these cases using the Clinical Frailty Scale. For corresponding time ranges, we measured the complexity of smart home data using a fixed-width sliding window and an entropy-based complexity index (Rényi Complexity Index) built on a Universal Sequence Map (USM-Rényi). Descriptive statistics and graphical analysis were used to describe intraindividual frailty and sensor complexity change.
Results: The complexity of sensor-observed indoor movement does change over time in older adults as quantified by the computational method. In some individuals, these changes track with health transitions and frailty progression. The trendsand monotonicity of complexity trajectories varied between cases . Overall, 3 of the cases demonstrated a negative association between frailty and complexity, while the association was not as clear for the other cases .
Conclusions: The complexity of older adults’ smart home data is highly diverse. Changes in health and frailty influence indoor movement complexity. Although the findings suggest a relationship between frailty and complexity, confounding factors, such as home layout, visitors, external events, and technology disruptions, may influence sensor signals.
JMIR Aging 2026;9:e77322; doi: 10.2196/77322 | Keywords: entropy; complexity; smart home; movement; frailty [https://aging.jmir.org/2026/1/e77322](https://aging.jmir.org/2026/1/e77322) JMIR Aging 2026 | vol. 9 | e77322 | p. 1 (page number not for citation purposes) JMIR AGING Introduction Background Frailty is a critical public health challenge among older adults globally. It is characterized as a clinically identifiable state of diminished physiologic reserve and heightened vulnerability to stressors and affects. An estimated 10%‐15% of community-dwelling individuals aged 65 years and older experience frailty, with the prevalence escalating to 51% among those aged 90 years and older [1] . This multifactorial syndrome, encompassing multiple impairments such as physical weakness, exhaustion, slow gait, low activity levels, and unintentional weight loss, elevat

FAQs

How does the study measure indoor movement complexity in smart homes?

Indoor movement complexity is quantified by calculating the entropy of smart home sensor data, using a fixed-width sliding window and an entropy-based complexity index built on a Universal Sequence Map (USM-Rényi).

What frailty assessment is used for the study cases?

Frailty is assessed weekly for the selected older adult cases using the Clinical Frailty Scale.

What relationship between complexity and frailty is reported?

In some individuals, changes in sensor-observed complexity track health transitions and frailty progression, and 3 of the cases show a negative association between frailty and complexity.

Sep 29, 2026

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