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Ankle moment plays an important role in human gait analysis, patients’ rehabilitation process monitoring, and the human-machine interaction control of exoskeleton robots. However, current ankle moment estimation methods mainly rely on inverse dynamics (ID) based on optical motion capture system (OMC) and force plate. These methods rely on fixed instruments in the laboratory, which are difficult to be applied to the control of exoskeleton robots. To solve this problem, this paper developed a new distributed plantar pressure system and proposed an ankle plantar flexion moment estimation method using the plantar pressure system. We integrated eight pressure sensors in each insole to collect the pressure data of the key area of the foot and then used the plantar pressure data to train four neural networks to obtain the ankle moment. The performance of the models was evaluated using normalized root mean square error (NRMSE) and cross-correlation coefficient (ρ). During experiments, eight subjects were recruited for the overground walking tests, and OMC and force plate were used as the gold standard. The results indicate that the Genetic algorithm - Gated recurrent unit estimation algorithm (GA-GRU) was the best estimation model which achieved the highest accuracy in generalized ankle moment estimation (NRMSE = 7.23%, ρ = 0.85) compared with the other models. The designed novel distributed plantar pressure system and the proposed method could serve as a joint moment estimation approach in wearable robot control and human motion state monitoring.

Internet of Things (IoT) messaging protocols play an important role in facilitating communications between users and IoT devices. Mainstream IoT platforms employ brokers, server-side implementations of IoT messaging protocols, to enable and mediate this user-device communication. Due to the complex nature of managing communications among devices with diverse roles and functionalities, comprehensive testing of the protocol brokers necessitates collaborative parallel fuzzing. However, being unaware of the relationship between test packets generated by different parties, existing parallel fuzzing methods fail to explore the brokers’ diverse processing logic effectively. This article introduces MPFuzz, a parallel fuzzing tool designed to secure IoT messaging protocols through collaborative packet generation. The approach leverages the critical role of certain fields within IoT messaging protocols that specify the logic for message forwarding and processing by protocol brokers. MPFuzz employs an information synchronization mechanism to synchronize these key fields across different fuzzing instances and introduces a semantic-aware refinement module that optimizes generated test packets by utilizing the shared information and field semantics. This strategy facilitates a collaborative refinement of test packets across otherwise isolated fuzzing instances, thereby boosting the efficiency of parallel fuzzing. We evaluated MPFuzz on six widely used IoT messaging protocol implementations. Compared to two state-of-the-art protocol fuzzers with parallel capabilities, Peach and AFLNet, as well as two representative parallel fuzzers, SPFuzz and AFLTeam, MPFuzz achieves (6.1%, $174.5\times $ ), (20.2%, $607.2\times $ ), (1.9%, $4.1\times $ ), and (17.4%, $570.2\times $ ) higher branch coverage and fuzzing speed under the same computing resource. Furthermore, MPFuzz exposed seven previously unknown vulnerabilities in these extensively tested projects, all of which have been assigned with CVE identifiers.