Advances in mobility assessment: A systematic review of technology-based evaluation in healthy older adults

Abstract

Background

By 2050, 22% of the global population will be aged 60 years or older. Aging is associated with physiological changes that can impair mobility and quality of life. Therefore, evaluating and improving mobility in older adults is crucial to maintaining well-being. Several methods currently exist for mobility assessment, increasingly supported by digital technologies. This systematic review aimed to investigate the technologies, both hardware and software, integrated for mobility assessment in healthy older adults.

Method

A literature search was performed for studies published between 2005 and 2023 in PubMed, IEEE Xplore, ScienceDirect, ACM Digital Library, and Springer. Articles focusing on pathological populations, fall assessment, or neuroscience/neuromuscular studies were excluded. Of 4199 studies retrieved, 36 met the inclusion criteria.

Results

Nine mobility assessment tests were identified, ranging from self-reported to performance-based tests. Sixteen studies integrated sensors to automate and quantify assessments, including inertial measurement units (IMUs), optical, ambient, pressure, and infrared sensors. Five studies used smartphones for mobility monitoring, while artificial intelligence was applied in seven studies: three applied machine learning for classification, one used probabilistic machine learning for walking speed estimation, and three used deep learning for skeletal detection or full automation. Two studies employed immersive technologies, and others explored alternative approaches such as animated video–based self-reports or telephone evaluations.

Conclusions

In conclusion, the emergence of sensors, smartphones, artificial intelligence and immersive technologies has provided promising avenues for more accurate and objective mobility assessment, ultimately leading to enhanced clinical interventions. Based on the findings of this systematic review, we particularly recommend that future work focus on developing fully automated mobility assessment systems that integrate deep learning algorithms with IMUs. This combination offers strong potential to improve the accuracy, reliability, and comprehensiveness of mobility evaluations by harnessing the rich data from IMUs and the advanced analytical power of deep learning.