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Chang et al. Soft Sci. 2026, 6, 29 Page 13 of 26

throwing), mechanical analysis requires integration of marker-based three-dimensional optical motion
capture systems with ground force platforms. By collecting kinematic data (e.g., joint angles and
displacements) and external force data (e.g., ground reaction forces), inverse dynamics algorithms enable
computation of net joint torques and other mechanical outputs, facilitating detailed biomechanical profiling
of dynamic movements. However, such laboratory paradigms are limited by high costs and dependence on
controlled scenarios (e.g., specialized research facilities), restricting their utility primarily to benchmarking
the accuracy of wearable-based mechanical estimation methods. In contrast, inertial measurement unit
(IMU) and smart insoles have emerged as core devices for monitoring muscle strength-related indicators in
dynamic scenes . Their integration enables inverse estimation of three-dimensional ground reaction force
[22]
(GRF), plantar pressure center, and derived metrics such as gait event timing and vertical stiffness (K ) .
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These indicators directly inform assessments of muscle fatigue (e.g., abnormal decline in vertical stiffness
indicating lower limb muscle fatigue) and recovery trajectories (e.g., symmetrical recovery of GRF reflecting
improved muscle strength balance) . Critically, these wearable systems operate independently of controlled
[133]
environments, supporting long-term continuous monitoring in naturalistic settings such as training grounds
and rehabilitation centers - thus addressing the contextual limitations of traditional laboratory techniques.

For soft tissue stiffness evaluation
Traditional laboratories employ ultrasonic shear wave elastography or strain imaging techniques. By emitting
shear waves into tissues and analyzing their propagation speeds, the equivalent modulus of the tissues is
quantified, achieving high measurement accuracy. However, this technology relies on large-scale equipment,
requires professional operation, and is restricted to static or controlled tasks - such as measuring muscle
contraction in fixed posture - failing to meet the demand for continuous recording in motion scenarios .
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Consequently, its adaptability across diverse settings remains limited. In recent years, wearable elastography
technology based on the mechanism of “electromechanical vibration-skin surface wave dispersion analysis”
has demonstrated significant advantages: wireless transmission, low power consumption, and
centimeter-level depth resolution. Importantly, its measurement results have been verified to align with those
of ultrasonic elastography [135] . This innovation enables continuous tracking of muscle stiffness during
dynamic activities, including walking and resistance training. For example, it detects localized stiffness
increase post-injury and gradual stiffness decrease during recovery. Nevertheless, further optimization is still
required to enhance dynamic accuracy and long-term stability.

Overall, wearable biomechanical sensors enable practical estimation of soft tissue stiffness in real-world
settings. However, their outputs are derived from indirect modeling rather than direct force measurement,
introducing uncertainty during highly dynamic activities. Sensor misalignment, soft tissue artifacts, and
biomechanical assumptions may further affect accuracy. Therefore, current wearable systems should be
considered complementary tools rather than replacements for gold-standard laboratory instrumentation.

It deserves to note that, in practical applications, wearable sensors do not typically diagnose muscle fatigue,
injury, or healing as discrete clinical labels. Instead, they quantify deviations from individualized
physiological baselines and monitor temporal patterns that may indicate transitions between functional
states. In competitive sports, sustained reductions in neuromuscular efficiency and abnormal recovery
trajectories may serve as early warning signs of injury risk following cumulative fatigue. In public physical
exercise settings, wearable systems can detect excessive training loads by identifying persistent elevations in
muscle activation relative to output performance. During rehabilitation and healing, progressive
normalization of mechanical and neuromuscular indicators may provide objective metrics for recovery
monitoring and safe return-to-activity decision-making. Thus, wearable technologies currently function
primarily as continuous monitoring and risk assessment tools rather than standalone diagnostic instruments.