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Sun et al. Soft Sci. 2025, 5, 18 https://dx.doi.org/10.20517/ss.2024.77 Page 19 of 26

neuromorphic computing technology also shows significant potential in wearable devices, particularly in
health monitoring. These smart health devices, including smartwatches and health-monitoring patches, use
neuromorphic algorithms to monitor real-time physiological signals and predict health conditions. By
modeling and predicting real-time signals through SNNs, these devices can detect early signs of health
issues. For instance, a SNN can predict a user’s exercise or fatigue state based on heart rate patterns, even
detecting potential health risks in advance. Researchers have developed a wearable, flexible sweat-sensing
platform for real-time, multi-channel sweat analysis. Integrated ion electrophoresis modules enabled
automatic and programmatic sweat extraction. These sensors held great potential for monitoring
dehydration, diagnosing cystic fibrosis, drug monitoring, and non-invasive blood glucose detection
[110]
[Figure 7F].

Robotic skin integrates skin-inspired sensors with neuromorphic computing, allowing robots to “feel” and
perceive their environment. Using SNNs, the system processes sensory signals and quickly adjusts the
robot’s actions based on tactile inputs such as touch pressure or object contact. A recent study designed a
flexible tactile sensing array based on capacitive mechanisms, with a polyethylene terephthalate (PET)
substrate, PDMS dielectric layer, and convex-concave contact layers. The PDMS layer featured four types of
microstructures, enabling tactile feedback control for robotic arms, showing potential for obstacle
[111]
avoidance .
The multimodal perception system is also a type of integrated system that combines skin-inspired sensors
and neuromorphic computing. These systems can simultaneously process tactile information, auditory
information, and simulate the comprehensive processing capabilities of human senses. Data from sensor
arrays, including tactile and sound sensors, are processed through neuromorphic computing units (e.g.,
SNN). The system can perceive and learn based on the combined patterns of touch and sound, enabling
more complex tasks such as intelligent voice control and touch-sound synchronization. Researchers have
combined pressure-activated organic electrochemical synaptic transistors with artificial mechanoreceptors
to detect the directional movement of object vibrations. By processing spike-encoded signals through a deep
learning model, the spatiotemporal characteristics of tactile patterns were effectively distinguished,
[112]
achieving high recognition accuracy [Figure 7G]. Another study proposed a multimodal bionic tactile
sensor module capable of sensing surface geometries, forces, vibrations, and temperatures, exploring and
mimicking human tactile perception capabilities .
[113]
In recent years, significant progress has been made in the development of multimodal sensing systems. At
the same time, such integrated systems often exhibit superior performance. We often focus on the system’s
skin-inspired neuromorphic sensors, materials, sensitivity or accuracy, power consumption, response time,
and their task to assess the system’s specific performance and the latest developments. An overview is
provided in Table 1. However, as integrated systems, they still face several key limitations and challenges.
Multimodal sensing systems typically require many sensors and significant computational resources,
resulting in high energy consumption. Furthermore, the manufacturing costs of current sensing systems
remain elevated, and their production processes are often complex, which presents a barrier to large-scale
deployment and widespread application. For e-skin and robotic skin, long-term wearability and comfort
still need further improvement. Specifically, in terms of simulating human skin, there is still room for
enhancement in softness, breathability, and overall comfort. Integrated systems require larger sensor arrays,
but these large arrays are prone to crosstalk, degraded performance, and difficulties in functional
integration. Current research systems are typically small-scale, and substantial advancements will require
further breakthroughs in materials, performance, and low energy consumption.