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A Computerized Functional Skills Assessment and Training Program Targeting Technology Based Everyday Functional Skills

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Published on: February 13, 2020

Personalized cognitive enhancement for older adults: An aging-friendly closed-loop human-machine interface framework.

Sa Zhou¹, Yang Liu¹, Adam Turnbull¹

¹Department of Psychiatry and Behavioral Sciences, Stanford University, United States.

Ageing Research Reviews

|August 24, 2025

Summary

This study introduces a closed-loop human-machine interface (HMI) framework to personalize digital non-pharmacological interventions (dNPIs) for cognitive enhancement in older adults, aiming for improved effectiveness and adherence.

Keywords:

Aging-friendly Closed-loop Cognitive enhancement Digitally delivered non-pharmacological interventions Human-machine interface Older adults Personalized intervention

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Assessment of Age-related Changes in Cognitive Functions Using EmoCogMeter, a Novel Tablet-computer Based Approach

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Assessment of Age-related Changes in Cognitive Functions Using EmoCogMeter, a Novel Tablet-computer Based Approach

Published on: February 14, 2014

Area of Science:

Neuroscience
Human-Computer Interaction
Gerontology

Background:

Digitally delivered non-pharmacological interventions (dNPIs) show promise for cognitive enhancement in older adults but lack personalization.
Inconsistent effectiveness stems from generic designs and unclear mechanisms of action, hindering prediction of individual cognitive gains.

Purpose of the Study:

To propose a closed-loop human-machine interface (HMI) framework for personalized dNPIs.
To optimize neurocognitive resource engagement for cognitive enhancement in aging populations.
To address challenges in neurobehavioral representation, intervention tailoring, and aging-friendly design for dNPIs.

Main Methods:

Outlining a closed-loop HMI framework with sensor, controller, and actuator components for real-time neurobehavioral monitoring and modulation.
Proposing a multimodal sensor network to capture central and peripheral nervous system activities.
Leveraging artificial intelligence (AI) for cognitive decoding and modulation using integrated neurobehavioral signals.

Main Results:

The framework integrates multimodal sensors and AI for decoding and modulating neurobehavioral activities.
It enables domain-specific tailoring of interventions for cognitive processes.
Focuses on aging-friendly design principles for usability, validity, reliability, and long-term adherence.

Conclusions:

The proposed closed-loop HMI framework offers a pathway to personalized dNPIs with enhanced effectiveness and scalability for older adults.
This approach aims to promote brain resilience and healthy longevity.
It advances the development of adaptive interventions tailored to individual neurobehavioral characteristics.