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Lu et al. Soft Sci 2024;4:36 https://dx.doi.org/10.20517/ss.2024.29 Page 17 of 20

models, such as finite element analysis (FEA) and multibody dynamics simulations, can simulate
biomechanical behavior based on known anatomical and mechanical properties. These models can be
calibrated and validated using experimental data obtained from non-invasive measurements. By combining
computational modeling with experimental measurements, researchers can gain insights into biomechanical
processes at various scales. For example, a MEM device integrating a vibration actuator and a soft strain
sensor is used to dynamically measure Young’s modulus of skin and other soft tissues. FEA is used to
quantify the mechanical coupling between actuators, sensors and tissues ; (4) The development of
[73]
miniaturized and wireless sensors offers the potential for unobtrusive and continuous monitoring of
biomechanics in vivo. These sensors can be implanted, attached to the skin, or even ingested to collect data
on parameters such as strain, pressure, and motion. Advancements in flexible and stretchable electronics
have facilitated the integration of such sensors with the human body. A typical example is a contact lens
with a fully integrated system that can operate wirelessly, battery-free, while detecting and transmitting
[10]
intraocular pressure to a mobile phone via standard NFC technology ; (5) Machine learning algorithms
and artificial intelligence techniques can assist in processing and analyzing large datasets generated by
hybrid measurement systems. These algorithms can identify patterns, predict behavior, and extract
meaningful information from complex biomechanical data, aiding in the development of more precise and
[82]
efficient evaluation methods. For instance, Kim et al. used algorithms to classify EMG signals . With
machine learning algorithms, soft electronics are increasingly intelligent and can achieve real-time analysis
and diagnosis.

The complexity and diversity of biological systems requires collaboration among diverse scientific
disciplines, including biology, chemistry, physics, and computer science. Innovating flexible wearables for
assessing the biomechanical properties of deep tissues is crucial for broader disease assessment. These
advancements will not only enhance current research but also facilitate future exploratory studies and
clinical applications.

DECLARATIONS
Authors’ contributions
Investigation, visualization, writing - original draft: Lu Y
Writing - original draft: Ma L
Investigation, visualization, data curation: Zhang H
Data curation: Mei Y, Xiong Z
Conceptualization, methodology, resources, supervision, writing - review and editing: Song E

Availability of data and materials
Not applicable.

Financial support and sponsorship
This work is supported by the STI2030-Major Project (2022ZD0209900), the National Natural Science
Foundation of China (62204057), the Science and Technology Commission of Shanghai Municipality
(22ZR1406400), and the Lingang Laboratory (LG-QS-202202-02). We also appreciate the support from the
State Key Laboratory of Integrated Chips and Systems (SKLICS-Z202306) and the Young Scientist Project of
the MOE Innovation Platform.

Conflicts of interest
All authors declare that there are no conflicts of interest.