Vibration energy harvesting is a promising solution for powering battery-free IoT systems; however, the instability of ambient vibrations presents significant challenges, such as limited harvested energy, intermittent power supply, and poor adaptability to various applications. To address these challenges, this paper proposes ViPSN2.0, a modular and reconfigurable IoT platform that supports multiple vibration energy harvesters (piezoelectric, electromagnetic, and triboelectric) and accommodates sensing tasks with varying application requirements through standardized hot-swappable interfaces. ViPSN 2.0 incorporates an energyindication power management framework tailored to various application demands, including light-duty discrete sampling, heavyduty high-power sensing, and complex-duty streaming tasks, thereby effectively managing fluctuating energy availability. The platform’s versatility and robustness are validated through three representative applications: ViPSN-Beacon, using an ultra-lowcost structural PZT (ϕ35 mm, <0.002 $) to enable a BLE advertisement from a single transient fingertip press with 100 m line of sight; ViPSN-LoRa, supporting wireless communication powered by wave vibrations in actual marine environments (Bohai Bay) with per-uplink task energy compatible with kilometer-scale field links; and ViPSN-Cam, enabling intermittent image capture and wireless transfer, delivering one frame approximately every 15 s under typical conditions. Experimental results demonstrate that ViPSN 2.0 can reliably meet a wide range of requirements in practical battery-free IoT deployments under energy-constrained conditions.

Human-motion energy harvesting is emerging as a promising solution for wearable electronics and devices, offering a sustainable power source that extends operational longevity and enhances durability. However, current techniques and prototypes have yet to achieve fully interactive, battery-free functionality. This paper presents a battery-free interactive gaming system powered by energy harvested from transient fingertip motion. To ensure the reactivity, interactivity, and stability of the fingertip motion harvester (FMH), we employ a multistable structure. The FMH unit provides a reliable energy solution by utilizing precharged potential energy within dynamically varying potential wells. Additionally, the integration of a bistable screen design facilitates seamless gaming experiences, decouples game logic from user interface mechanics, and ensures rapid system recovery after power interruptions. Beyond advancing fundamental research, this work pioneers a practical battery-free interaction paradigm based on fingertip motion, with potential for broader battery-free user interfaces and low-power interactive systems.

Representation learning in spatial and temporal domains has shown significant potential in EEG decoding, advancing the field of brain-computer interfaces (BCIs). However, the critical role of frequency information, closely tied to the brain's neurological mechanism, has been largely neglected. In this paper, we propose FSTNet, which integrates frequency-spatial-temporal domains synergistically. The network allows broadband EEG signals as input and adaptively learns informative frequency signatures. A frequency-aware module emphasizes the importance of frequency information by selectively assigning weights to latent representations in the frequency space. Subsequently, self-attention captures spatial and temporal dependencies, extracting discriminative neural signatures for EEG decoding. We conducted extensive experiments on EEG datasets for motor imagery and emotion recognition, achieving superior results on SEED, PhysioNet, and OpenBMI datasets in both individual and cross-subject scenarios. Additionally, visualization reveals that the network captures informative frequency ranges and spatial patterns associated with specific tasks, aligning with known physiological mechanisms. This enhances the transparency of the network's learning process. In conclusion, our method exhibits the potential for decoding EEG and advancing the understanding of neurological processes in the brain.

This demonstration showcases a battery-free wireless keyboard that utilizes the kinetic energy generated from key presses to generate electrical power. Each key incorporates a Quasi-Static Toggling harvester, which employs potential energy pre-charging to ensure a consistently reliable energy output. Extensive practical testing has confirmed that this keyboard exhibits responsiveness and low latency comparable to traditional wireless keyboards. This study represents a significant improvement over previous battery-free IoT applications that often compromised service quality, as it offers a stable energy-harvesting mechanism and provides a dependable framework for designing battery-free devices.

Vibration energy harvesters have been widely investigated to supply sustainable power for devices in the Internet of Things (IoT). Despite the advances in vibration energy harvesters, there still remain challenges in the design and optimization of energy harvesters to meet the increasing power demand of long-range IoT applications. In this article, an auxetic piezoelectric energy harvester with tapered thickness (TAEH) is proposed to improve the efficiency of energy harvesting by achieving the uniform stress and high average stress. Compared with traditional auxetic piezoelectric energy harvester with uniform thickness (UAEH), the stress distribution with tapered thickness is more uniform, which can contribute to a higher power output with lower maximum stress. Furthermore, the multiobjective optimization is used to further improve the average stress values without increasing the maximum stress, thus increasing the energy output. Finite element analysis is performed to validate the performance of the energy harvesters. In the experimental validation, it is found that with the tapered thickness introduced to the auxetic energy harvester, the maximum power output and power density of TAEH can be increased by 212.84% and 279.08%, respectively, compared with UAEH. After the optimization, these two indices of the optimized TAEH (OTAEH) are further increased by 24.32% and 27.59%, respectively. Specifically, a high power density of 0.148 mW/g is achieved in the OTAEH, indicating its high vibration energy harvesting performance with lightweight. Finally, it is demonstrated that the OTAEH can generate more than 40.94 mJ within 27.6 s to successfully power an IoT device for temperature sensing and long-range data transmission.

For boosting output power, frequency-up methods like gear train and plucking are introduced into pendulum-based electromagnetic energy harvesters (EEHs). Still, the gear train and plucking methods have the problems of high manufacturing costs and high energy loss, respectively. To address the above issues, inspired by biological grooming behavior, we present a low-cost, high-efficiency EEH utilizing 3D printing. This device is excited by an asymmetric stiffness pendulum, induced by an oblique cantilever beam, converting the ultra-low frequency of human motion into high-speed, unidirectional rotation of the rotor. The energy density comparison between the sandwich and back iron structure of the EEH is made by the simulation, and then parameters are selected. The bench-top swing experiment system is established to test the performance of the EEH, and the established equivalent electromagnetic model discusses the load resistance result. The feasibility of human walking for powering the Internet of Things (IoT) device is explored when wearing the EEH. The bench-top test result shows that the EEH can reach the normalized power of 1.07 · 10-4 W/(Hz ·°) and normalized power density of 6.6 W/(m3·°). Through human testing at a walking of 1 km/h, the IoT device can run normally, showing great potential for achieving self-power and cost-effective IoT devices.

Condition monitoring has played a significant role in reducing downtime and maintenance costs for key rotating machinery. However, traditional methods to power wireless sensor nodes depend highly on capacity-limited batteries or wiring from external source. Although the rotational energy harvesting technologies have been widely considered as a promising self-powered method, the output power under low-frequency occasions fails to supply the usable energy to wireless sensor nodes for condition monitoring. Therefore, a self-powered wireless condition monitoring system for rotating machinery in low-frequency occasions is presented in this article. A variable reluctance energy harvester is designed to convert rotational motion into electrical power. The ring-shaped stator contains magnets and tile silicon steel, while the rotor is composed of teethed silicon steel and coils. Besides, a ring-shaped circuit for low-speed occasions is designed to achieve power management and storage, signal detection and wireless transmission. In addition, an experimental test is carried out to verify the performance of the proposed self-powered wireless condition monitoring system. The results show that the output power of the proposed harvester reaches 336.7–851.8 mW at 200–328 rpm, while the average power after rectification and filtering is 203.3–602.5 mW. Moreover, the power test results show that the broadcasting, connecting, collecting and transmitting, and sleep modes of WiFi consume around 450, 206, 313, and 46 mW, respectively. By properly prolonging sleep mode, the average power consumption of wireless sensor networks can be significantly reduced. Furthermore, the condition monitoring performance of the proposed self-powered system is verified by acceleration detection.

Various active back-support exoskeletons have been developed to assist manual materials handling work for low back injury prevention. Existing back-support exoskeleton actuation either suffers from rigid transmission structure, or fails to efficiently generate assistance via portable actuation system with flexible transmissions. In this paper, a novel cable-driven series elastic actuation (CSEA) system is proposed to realize a flexible and portable back-support exoskeleton design with safe, efficient, and sufficient assistive torque output capability. The CSEA system realizes a flexible actuation based on cable transmission for an ergonomic human-exoskeleton interaction. Based on a torsion spring-support beam mechanism, it achieves an efficient assistance output capability to prevent high cable force demand and resultant lumbar compression, assuring a safe and synergistic operation for flexible exoskeleton actuation. Meanwhile, this mechanism enables the CSEA system to integrate series elastic actuator (SEA) with cable transmission and operates with multiple statuses to leverage SEA advantages and to overcome its torque output limitation. Dynamic model is established for the CSEA system, and a unified torque controller is designed for stable, continuous, and accurate torque control of the CSEA system despite its discontinuous dynamics during operation status transition. The efficacy of the closed-loop CSEA system to enable an ergonomic and efficient back-support exoskeleton actuation with the capability of accurately delivering desired level of assistance is verified via bench tests and human tests. Results verified that the CSEA system actuated exoskeleton can effectively reduce activity of relevant muscles during trunk flexion and extension motions compared to no exoskeleton case, validating successful application of the CSEA system on the exoskeleton for an effective back support effect.

Energy harvesting from human motion has great potential of sustainably powering Internet of Things (IoT) sensors and satisfying their continuous sensing requirement. In this article, we propose a high-performance wrist-worn energy harvester to efficiently capture the biomechanical energy of arm swinging to self-power wearable sensors. Based on coaxial topology, a planetary gear system serves as frequency-up converter to increase energy conversion capacity, and all the functional units are coaxially installed to achieve a highly compact structure. Thanks to improved energy conversion capacity and structure compactness, the energy harvester can efficiently capture the kinetic energy of arm swinging and achieve high average power. We derive an analytical model to predict the system dynamics and power generation performance using the Lagrangian approach and mirror image method. We fabricate a miniature prototype with different proof mass configurations, which is characterized under bench-top excitations and tested under real walking excitations. The results show that in bench-top testing, the prototype generates maximum average power of 2.73 mW and maximum power density of 535.29$\mathbf {\mathrm {\mu }}\text{W}$/cm3 at the excitation frequency of 1.2 Hz among different configurations. In terms of average power and power density, this energy harvester significantly overperforms its counterparts. In real walking testing, the prototype generates maximum average power of 3.13 mW at a walking frequency of 1.2 Hz among different configurations and achieves higher power output than bench-top testing. Finally, the prototype is used to simultaneously power four sensors and demonstrates great potential in self-powered IoT applications.

Inertial energy harvesters have great potential to sustainably power wearable sensors for the Internet of Things. However, the bulky proof mass used to capture kinetic energy limits the power density of such an energy harvester. Targeted for high-power density, we propose an inertial energy harvester without additional proof mass to efficiently scavenge the kinetic energy of human limb swing. By adopting a planetary structure, the power generation unit, comprising the base, coils, rotor, and magnets, serves as an eccentric weight of the system, so no additional proof mass is needed. Excited by limb swing, the power generation unit oscillates along the sun gear so that the rotor is actuated by the sun gear to spin and produce electricity. We build a theoretical model to predict the average output power under limb swing excitations. We fabricate a miniature prototype to experimentally characterize the energy harvester under pseudowalking excitation and evaluate its performance in real walking. The results show that the prototype generates a maximum power of 1.46 mW and power density of $454.82~{\mu }\text{W}$ /cm3in pseudowalking testing, which are over ten times those of its counterparts. In the real walking test, the prototype performed even better, achieving 1.84 and 2.95 mW when worn on the wrist and ankle, respectively. After power regulation, the energy harvester can fully power a pedometer at various walking speeds. Finally, simulations using real walking data demonstrate that the proposed device reaches higher power density compared with conventional structures and that introducing the additional proof mass unnecessarily increases power density.

Evaluation of individual gait pattern is important for both abnormal gait diagnosis and gait rehabilitation in mobility impaired people. In this paper, semi-Markov process (SMP) is applied to model and analyze human gait in level walking. Gait states are detected from ground reaction forces (GRFs), and gait cycles are described as state transitions in a gait Markov chain (GMC) with sojourn times. Several gait features are defined and online estimated based on the SMP model. With this model, abnormal gait patterns are further analyzed and indexes for gait abnormality assessment are proposed. Experiments of gait analyses with proposed method are conducted on subjects with different health conditions. Results show that individual gait pattern can be successfully obtained and evaluated. Potential applications in gait diagnosis and powered lower limb orthosis (PLLO) control for gait assistance are also discussed.

This article introduces a novel perspective to the study of Class-E power amplifier by extending the scope of electrical impedance. Owing to this extension, the electrical property of the active switch in a Class-E circuit is taken as a special kind of impedance. Consequently, it is possible to regard a whole Class-E circuit as combination of impedances, whose total impedance is obtainable according to the existing circuit laws. When subjected to a DC supply voltage, the steady-state response of the total impedance can be directly obtained with the Ohm's law, regardless of the transient state. In addition, based on the formulation, regarding maximum power conversion efficiency as its design objective, Class-E optimization is also carried out. Since no waveform equation is required for both simulation and optimization, these simulation and optimization are more concise and efficient than those in all of the previous studies.

One of the challenging aspects of utilizing magnetorheological (MR) dampers to achieve high level of performance is the development of accurate models and control algorithms that can take advantages of the unique characteristics of these devices because of their inherent nonlinearity. In this paper, the authors proposed a direct identification and an inverse dynamic modeling method for MR dampers using recurrent neural networks. Based on the above neural network models, a configuration for the MR damper controller is also explored. The command voltage for the MR damper can be obtained through the neural network model according to the desired damping force determined from the system controller. The architectures and the learning methods of the direct and inverse dynamic neural network models for the MR damper are presented, and some simulation results about the MR damper controller are discussed.