Search Results (10)

Assessment of a badminton player’s footwork is critical. However, the automated footwork assessment method is lacking. The purpose of the study is to investigate how seismographs can be used to collect vibration signals to locate the footsteps of a player on the badminton court. Four seismographs are positioned at the four corners of the badminton court to acquire the vibration signals of two players’ footsteps. After signal preprocessing, multiple features are extracted from the preprocessed vibration signals, including the maximum amplitude ${AMP}_{\mathrm{m}\mathrm{a}\mathrm{x}}$, the index of the maximum amplitude ${IND}_{\mathrm{m}\mathrm{a}\mathrm{x}}$, and area under the waveform of the signal $AUW$. The latter two features are selected to predict the localization of the footstep after correlation analysis of the features. A multilayer perceptron (MLP) and a support vector machine (SVM) are trained to combine all the features to predict the locations of the footsteps into one of the eighteen zones of the badminton court. Six-fold and leave-one-out (LOO) cross-validations are used to estimate the accuracy of the localization method. All three extracted features are correlated with the footstep location, and ${AMP}_{\mathrm{m}\mathrm{a}\mathrm{x}}$ and $AUW$ are highly correlated. Both the six-fold and LOO cross-validations indicate that the overall accuracy is 98–99%, using either the MLP or the SVM. These promising results indicate that the proposed approach has a potential to trace badminton player’s footwork accurately and future studies are warranted to investigate the utilities of the vibration signals in badminton player’s footwork assessment. Full article

To address the limitations of traditional footstep vibration signal localization algorithms, such as limited accuracy, single feature extraction, and cumbersome parameter adjustment, a motion target localization method for step vibration signals based on deep learning is proposed. Velocity vectors are used to describe human motion and adapt it to the nonlinear motion and complex interactions of moving targets. In the feature extraction stage, a one-dimensional residual convolutional neural network is constructed to extract the time–frequency domain features of the signals, and a channel attention mechanism is introduced to enhance the model’s focus on different vibration sensor signal features. Furthermore, a bidirectional long short-term memory network is built to learn the temporal relationships between the extracted signal features of the convolution operation. Finally, regression operations are performed through fully connected layers to estimate the position and velocity vectors of the moving target. The dataset consists of footstep vibration signal data from six experimental subjects walking on four different paths and the actual motion trajectories of the moving targets obtained using a visual tracking system. Experimental results show that compared to WT-TDOA and SAE-BPNN, the positioning accuracy of our method has been improved by 37.9% and 24.8%, respectively, with a system average positioning error reduced to 0.376 m. Full article

Quantitative analysis of human gait is critical for the early discovery, progressive tracking, and rehabilitation of neurological and musculoskeletal disorders, such as Parkinson’s disease, stroke, and cerebral palsy. Gait analysis typically involves estimating gait characteristics, such as spatiotemporal gait parameters and gait health indicators (e.g., step time, length, symmetry, and balance). Traditional methods of gait analysis involve the use of cameras, wearables, and force plates but are limited in operational requirements when applied in daily life, such as direct line-of-sight, carrying devices, and dense deployment. This paper introduces a novel approach for gait analysis by passively sensing floor vibrations generated by human footsteps using vibration sensors mounted on the floor surface. Our approach is low-cost, non-intrusive, and perceived as privacy-friendly, making it suitable for continuous gait health monitoring in daily life. Our algorithm estimates various gait parameters that are used as standard metrics in medical practices, including temporal parameters (step time, stride time, stance time, swing time, double-support time, and single-support time), spatial parameters (step length, width, angle, and stride length), and extracts gait health indicators (cadence/walking speed, left–right symmetry, gait balance, and initial contact types). The main challenge we addressed in this paper is the effect of different floor types on the resultant vibrations. We develop floor-adaptive algorithms to extract features that are generalizable to various practical settings, including homes, hospitals, and eldercare facilities. We evaluate our approach through real-world walking experiments with 20 adults with 12,231 labeled gait cycles across concrete and wooden floors. Our results show 90.5% (RMSE 0.08s), 71.3% (RMSE 0.38m), and 92.3% (RMSPE 7.7%) accuracy in estimating temporal, spatial parameters, and gait health indicators, respectively. Full article

This paper presents an occupant localization technique that determines the location of individuals in indoor environments by analyzing the structural vibrations of the floor caused by their footsteps. Structural vibration waves are difficult to measure as they are influenced by various factors, including the complex nature of wave propagation in heterogeneous and dispersive media (such as the floor) as well as the inherent noise characteristics of sensors observing the vibration wavefronts. The proposed vibration-based occupant localization technique minimizes the errors that occur during the signal acquisition time. In this process, the likelihood function of each sensor—representing where the occupant likely resides in the environment—is fused to obtain a consensual localization result in a collective manner. In this work, it becomes evident that the above sources of uncertainties can render certain sensors deceptive, commonly referred to as “Byzantines.” Because the ratio of Byzantines among the set sensors defines the success of the collective localization results, this paper introduces a Byzantine sensor elimination (BSE) algorithm to prevent the unreliable information of Byzantine sensors from affecting the location estimations. This algorithm identifies and eliminates sensors that generate erroneous estimates, preventing the influence of these sensors on the overall consensus. To validate and benchmark the proposed technique, a set of previously conducted controlled experiments was employed. The empirical results demonstrate the proposed technique’s significant improvement ($\tilde{3}$0%) over the baseline approach in terms of both accuracy and precision. Full article

Piezoelectric energy transducers offer great potential for converting the vibrations of pedestrian footsteps or cars moving on a bridge or road into electricity. However, existing piezoelectric energy-harvesting transducers are limited by their poor durability. In this paper, to enhance this durability, a piezoelectric energy transducer with a flexible piezoelectric sensor is fabricated in a tile protype with indirect touch points and a protective spring. The electrical output of the proposed transducer is examined as a function of pressure, frequency, displacement, and load resistance. The maximum output voltage and maximum output power obtained were 6.8 V and 4.5 mW, respectively, at a pressure of 70 kPa, a displacement of 2.5 mm, and a load resistance of 15 kΩ. The designed structure limits the risk of destroying the piezoelectric sensor during operation. The harvesting tile transducer can work properly even after 1000 cycles. Furthermore, to demonstrate its practical applications, the tile was placed on the floor of an overpass and a walking tunnel. Consequently, it was observed that the electrical energy harvested from the pedestrian footsteps could power an LED light fixture. The findings suggest that the proposed tile offers promise with respect to harvesting energy produced during transportation. Full article

Piezoelectric Vibration converters are nowadays gaining importance for supplying low-powered sensor nodes and wearable electronic devices. Energy management interfaces are thereby needed to ensure voltage compatibility between the harvester element and the electric load. To improve power extraction ability, resonant interfaces such as Parallel Synchronized Switch Harvesting on Inductor (P-SSHI) have been proposed. The main challenges for designing this type of energy management circuits are to realise self-powered solutions and increase the energy efficiency and adaptability of the interface for low-power operation modes corresponding to low frequencies and irregular vibration mechanical energy sources. In this work, a novel Self-Powered (SP P-SSHI) energy management circuit is proposed which is able to harvest energy from piezoelectric converters at low frequencies and irregular chock like footstep input excitations. It has a good power extraction ability and is adaptable for different storage capacitors and loads. As a proof of concept, a piezoelectric shoe insole with six integrated parallel piezoelectric sensors (PEts) was designed and implemented to validate the performance of the energy management interface circuit. Under a vibration excitation of 1 Hz corresponding to a (moderate walking speed), the maximum reached efficiency and power of the proposed interface is 83.02% and 3.6 mW respectively for the designed insole, a 10 k$\Omega$ resistive load and a 10 μF storage capacitor. The enhanced SP-PSSHI circuit was validated to charge a 10 $\mathsf{\mu }$F capacitor to 6 V in 3.94 s and a 1 mF capacitor to 3.2 V in 27.64 s. The proposed energy management interface has a cold start-up ability and was also validated to charge a (65 mAh, 3.1 V) maganese dioxide coin cell Lithium battery (ML 2032), demonstrating the ability of the proposed wearable piezoelectric energy harvesting system to provide an autonomous power supply for wearable wireless sensors. Full article

There is an interest in harvesting energy from people’s footsteps in crowded areas to power smart electronic devices with low consumption. The average power consumption of these devices is approximately 10 μW. The energy from our footsteps is green and free, because walking is a routine activity in everyday life. The energy floor is one of the most efficient pieces of equipment in vibration-based energy harvesting. The paper aims to improve the previous design of the energy floor—called Genpath—which uses a rotational electromagnetic (EM) technique to generate electricity from human footsteps. The design consists of two main parts of (1) the EM generator, including the lead-screw mechanism for translation-to-rotation conversion, and (2) the Power Management and Storage (PMS) circuit. The improvement was focused on the part of the EM generator. A thorough investigation of the design components reveals that the EM generator shaft in the previous Genpath design cannot continuously rotate when the floor-tile reaches the bottom end, resulting in no energy gain. Therefore, a one-way clutch is implemented to the system to disengage the generator shaft from the lead-screw motion when the floor-tile reaches the allowable displacement. During the disengagement, the EM generator shaft still proceeds with a free rotation and could generate more power. In our analysis, the dynamic model of the electro-mechanical systems with the one-way clutch was successfully developed and used to predict the energy performances of the VEH floors and fine-tune the design parameters. The analytical result is shown that the spring stiffness mainly affects the force transmitted to the EM generator, and then the induced voltage and power of the generator, thus, the value of the stiffness is one of the critical design parameters to optimize. Finally, the new prototype consisting of 12-V-DC generator, mechanisms of lead-screw and clutch, as well as coil springs with the optimal stiffness of 1700 N/m was built and tested. The average energy produced by the new prototype is 3637 mJ (or average power of 3219 mW), per footstep which is 2935 mJ greater than that of the previous design. Moreover, to raise the social awareness about energy usage, the sets of Genpath have been used to organize an exhibition, “Genpath Empower our Journey”. The people who stroll forward on the paths can realize how much energy they gain from their footsteps. Full article

Pedestrian counting has attracted much interest of the academic and industry communities for its widespread application in many real-world scenarios. While many recent studies have focused on computer vision-based solutions for the problem, the deployment of cameras brings up concerns about privacy invasion. This paper proposes a novel indoor pedestrian counting approach, based on footstep-induced structural vibration signals with piezoelectric sensors. The approach is privacy-protecting because no audio or video data is acquired. Our approach analyzes the space-differential features from the vibration signals caused by pedestrian footsteps and outputs the number of pedestrians. The proposed approach supports multiple pedestrians walking together with signal mixture. Moreover, it makes no requirement about the number of groups of walking people in the detection area. The experimental results show that the averaged F1-score of our approach is over 0.98, which is better than the vibration signal-based state-of-the-art methods. Full article

In renewable energy technology development, piezoelectric material has electro-mechanical converted capability and the advantages of simple construction and compact size, it has potential development since the environment vibration can be transferred into an electrical energy in daily harvesting applications. To improve the electro-mechanical converted efficiency of a piezoelectric harvester at low-frequency environment, a free vibration type of piezoelectric cantilever harvesting structure was proposed, which can generate a resonant oscillation by releasing an initial deformed displacement, and was uninfluenced from the effects of external environment. To analyze the harvesting behaviors, an equivalent circuit with voltage source was provided, and the parameters in theoretical model can be determined by the dimensions of the piezoelectric unimorph plate and its initial deformation. From the comparison of measurement and simulation, it reveals a significant efficient theoretical model where 8% error occurrence for storage energy was found. Finally, the proposed free-vibration generation method was developed in a piezoelectric harvesting floor design, which can transfer human walking motion into electric energy, and store in an external storage capacitor. From the testing result, one time of footstep motion can cause the charging energy in a 33 μF of storage capacitor achieve to 0.278 mJ, which was larger than the driven power of the wireless transmitter module, and then the wireless transmitter can be driven to send a RF signal without external power supply. Therefore, the designed piezoelectric harvesting floor has potential development to locate the user’s current position, which can provide users with future appropriate service for intelligent building application. Full article

This work investigated the mechanical and electrical behaviors of piezoelectric and triboelectric energy harvesters (PEHs and TEHs, respectively) as potential devices for harvesting impact-driven energy. PEH and TEH test benches were designed and developed, aiming at harvesting low-frequency mechanical vibration generated by human activities, for example, a floor-tile energy harvester actuated by human footsteps. The electrical performance and behavior of these energy harvesters were evaluated and compared in terms of absolute energy and power densities that they provided and in terms of these energy and power densities normalized to unit material cost. Several aspects related to the design and development of PEHs and TEHs as the energy harvesting devices were investigated, covering the following topics: construction and mechanism of the energy harvesters; electrical characteristics of the fabricated piezoelectric and triboelectric materials; and characterization of the energy harvesters. At a 4 mm gap width between the cover plate and the stopper (the mechanical actuation components of both energy harvesters) and a cover plate pressing frequency of 2 Hz, PEH generated 27.64 mW, 1.90 mA, and 14.39 V across an optimal resistive load of 7.50 kΩ, while TEH generated 1.52 mW, 8.54 µA, and 177.91 V across an optimal resistive load of 21 MΩ. The power and energy densities of PEH (4.57 mW/cm³ and 475.13 µJ/cm³) were higher than those of TEH (0.50 mW/cm³, and 21.55 µJ/cm³). However, when the material cost is taken into account, TEH provided higher power and energy densities per unit cost. Hence, it has good potential for upscaling, and is considered well worth the investment. The advantages and disadvantages of PEH and TEH are also highlighted as main design factors. Full article