Automatic indoor human tracking has gained significant research attention due to the growing demand for enhanced services in smart home environments. In this study, we present a novel method ...utilizing low-cost floor sensors to estimate an individual's position in a closed environment. The proposed approach involves calculating the center-of-mass (COM) curve based on the collected footsteps data, resulting in acceptable accuracy with low computational cost for both curved and straight paths. Additionally, an innovative approach based on a human walking model is introduced, effectively reducing floor sensors' output error by up to 26%, specifically for straight paths. We believe that this method paves the ground for future research endeavors on upscaling low-resolution sensors to higher resolutions and improving floor-sensor-based localization.
IoT and smart home applications demand for simultaneous sensing and energy independence. Here, a screen printable P(VDF-TrFE) cantilever based smart floor design, capable of sensing (sens) and acting ...as a piezoelectric energy harvester (PEH) is introduced. In a height of only 1 mm, which is well-suited for indoor floor applications, a sens-PEH cantilever optimization is performed by finite element method (FEM) simulations and experimentally validated; bending cantilevers perform much better compared to normal compressions modes with apparent sensitivities to be as high as 98 nC/N for 3.6 mm long cantilevers. This is more than three orders of magnitude larger compared to conventional flat P(VDF-TrFE) film sensors. When energy harvesting is of interest, the simulations show that short cantilevers provide the most energy. Using multilayered screen-printed devices and an active area of 8 cm2, such harvesters provide up to 18.4 µJ of step energy in the experiment, from which 6 µJ is storable in a capacitor. From the combined FEM and experimental results, the optimum sens-PEH geometry, either for sensing or harvesting, can be chosen and implemented in respective smart floor applications.
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•A smart floor device with energy harvesting and sensing capabilities is presented.•The device is highly integrated, with a thickness of several millimeters.•The active layer is a printed P(VDF-TrFE) flexible layer.•Sensitivity of 98 nC/N and up to 6 µJ per step can be achieved•A full demonstration of a smart floor is provided
Smart flooring systems are important components of intelligent building for purposes such as health care and security. It is finding that its every step more harshly spotlighted than has ever because ...it is subtle and intimate to people who come into contact with them. Here we presented a floor tracking system using a large-area single-electrode triboelectric nanogenerator by simply coupling commercial polyvinyl chloride (PVC) flooring with a thin copper film. The system is not only able to collect and convert ambient mechanical energy to electricity with a power-density of 0.5µA/cm2, but also enables identifying and tracking footstep patterns by analyzing the movement of the users within a specific area. A detailed architecture and implementation of the smart floor is proposed together with an exhaustive test of its multifunction to give a comparison with other solutions already known to the state of art. Besides, the proposed system can also help to make the environment comfortable for high quality of life, as demonstrated by its excellent sterilization and dust removal performance. These multifunctional characteristics making it ideal for many applications including security surveillance, patient monitoring, indoor positioning, asset tracking, and entertainment.
A self-powered smart floor was designed by coupling triboelectric nanogenerator (TENG) with a wireless transmission system. The minimalist design endows such smart floors several features such as low cost and simple structure. Besides activity recognition, security monitoring, trajectory tracking, and intelligent switching, the floor also shows excellent sterilization and dust removal performance. The system offers a convenient way for monitoring and improving of the indoor environment. Display omitted
•A smart flooring system has been developed for intelligent home.•Self-driven is achieved by collecting the mechanical energy from human motion .•Multifunction has been realized with sound working consistency.•Excellent performance for sterilization and dust removal has been demonstrated.
An idealized detailed 2D formulation is presented for suppression of transient impact sound transmission across a hybrid smart double-leaf sandwich beam (floor-ceiling) structure into a rectangular ...(receiving) room with ideally flat and rigid walls. The smart double wall structure, which is mechanically inter-connected at an arbitrary point with a lightweight nonlinear energy sink (NES) absorber, incorporates spatially distributed and electrically independent non-collocated semiactive (electro-rheological fluid- or ERF-incorporated) and fully-active (piezoceramic- or PZT-incorporated) actuator layers functioning in a closed loop control framework. Extensive time-domain numerical simulations initially calculate both the uncontrolled and controlled transient acoustic pressure fields in absence of the dynamic vibration absorber for four separate settings of the active (PZT-) and semiactive (ERF-) actuation elements. Subsequently, the remarkable performance of the GA-optimized hybrid smart active/semi-active/passive (PZT/ERF/NES) configuration, which benefits from the multi-mode targeted energy transfer (TET) mechanism of the NES, in significant broadband (low frequency) attenuation of the transmitted shock energy with a much lower actuator energy demand, is demonstrated. Furthermore, some important aspects of the transient fluid-structure interaction (TFSI) control problem like weakening of the acoustic shock focusing effects (focal zones) within the source room are illustrated through selected early-to-late-times 2D images and animations of the cavity pressure fields. Limiting situations are studied and correctness of the derivations is established against accessible data in addition to numerical (FEM) simulations.
This study explores task-specific ionic liquids (TSILs) in smart floor systems, highlighting their strong electrical rectification abilities and previously established wood preservative properties. ...Two types of TSILs, featuring a “sweet” anion and a terpene-based cation, were used to treat selected wood samples, allowing for a comparison of their physical and electrical performance with untreated and commercially treated counterparts. Drop shape analysis and scanning electron microscopy were employed to evaluate the surface treatment before and after coating. Near-IR was used to confirm the presence of a surface modifier, and thermogravimetric analysis (TGA) was utilized to assess the thermal features of the treated samples. The different surface treatments resulted in varied triboelectric nanogenerator (TENG) parameters, with the molecular structure and size of the side chains being the key determining factors. The best results were achieved with TSILs, with the instantaneous voltage increasing by approximately five times and the highest voltage reaching 300 V under enhanced loading. This work provides fresh insights into the potential application spectrum of TSILs and opens up new avenues for directly utilizing tested ionic compounds in construction systems.
The lack of physical exercise is among the most relevant factors in developing health issues, and strategies to incentivize active lifestyles are key to preventing these issues. The PLEINAIR project ...developed a framework for creating outdoor park equipment, exploiting the IoT paradigm to build "Outdoor Smart Objects" (OSO) for making physical activity more appealing and rewarding to a broad range of users, regardless of their age and fitness. This paper presents the design and implementation of a prominent demonstrator of the OSO concept, consisting of a smart, sensitive flooring, based on anti-trauma floors commonly found in kids playgrounds. The floor is equipped with pressure sensors (piezoresistors) and visual feedback (LED-strips), to offer an enhanced, interactive and personalized user experience. OSOs exploit distributed intelligence and are connected to the Cloud infrastructure by using a MQTT protocol; apps have then been developed for interacting with the PLEINAIR system. Although simple in its general concept, several challenges must be faced, related to the application range (which called for high pressure sensitivity) and the scalability of the approach (requiring to implement a hierarchical system architecture). Some prototypes were fabricated and tested in a public environment, providing positive feedback to both the technical design and the concept validation.
A smart floor with 16 embedded pressure sensors was used to record 420 simulated fall events performed by 60 volunteers. Each participant performed seven fall events selected from the guidelines ...defined in a previous study. Raw data were grouped and well organized in CSV format.
The data was collected for the development of a non-intrusive fall detection solution based on the smart floor. Indeed, the collected data can be used to further improve the current solution by proposing new fall detection techniques for the correct identification of accidental fall events on the smart floor.
The gathered fall simulation data is associated with participants’ demographic characteristics, useful for future expansions of the smart floor solution beyond the fall detection problem.
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•A force-sensing based floor panel is designed for indoor localization applications.•Corresponding localization algorithms and calibration schemes are designed and ...realized.•Positioning accuracy can be up to ±1 cm over a 1 m span.•Demos in robot localizations, center of gravity measurement, and data fusion are done for promoting applications.
Accurate position detection of indoor objects is crucial for many intelligent life applications and can be realized by various approaches. In certain applications, indoor position detection can be performed using some forms of touch-based floor sensing systems to avoid problems faced by using vision or inertial navigation approaches. The present study develops a floor panel test stage (1 × 1 m2) and its associated localization schemes based on rigid body statics for evaluating the feasibility and the positioning performance on determining the indoor locations of moving objects such as mobile robots. The motion signals are detected by four load cells located at the panel corners and are used to determine the position of the applied forces by means of rigid body statics. Essential tests are performed to examine the system performance. Noticed that due to the existence of boundary reaction moments, traditional predictions based on rigid body statics may not be sufficient and a calibration procedure is required. In parallel, for accommodating different possible scenarios, the corresponding associated localization schemes for estimating the force-applied locations based on the readout history and algorithms are also proposed and realized. The results indicate that the localization accuracy is approximately ± 1 cm over a 1 m span after calibration. Although the method is essentially static, it can still detect object motion provided the motion frequency less than 2 Hz. Furthermore, demonstrations are also presented to highlight other possible applications such as center of gravity measurement of objects and the fusion of multiple localization schemes in indoor environment. Finally, an indoor localization demonstration based on both wiimote IR LED and this smart floor is presented to elucidate the possible application of this work in sensor fusion for indoor motion control.
Significant research efforts have been directed into smart home environments in the last decade creating abundant opportunities for the broader home services ecosystem to foster a wide range of ...innovative services. Research interest has been given on automatic identification and tracking of people within the home environment to support customised services such as care services for elderly and disadvantaged people to enable and prolong their independent living. Although various approaches have been proposed to tackle this problem, solutions still remain elusive due to various reasons (e.g. user acceptance). Literature reviews have indicated the need for an advanced non-tagged identification and tracking approach that is capable to provide the infrastructure support for realisation of context-aware services, satisfy users’ needs, and deal with the complexity of smart home environmental conditions. The aim of this study is to develop and implement an advanced approach that is capable to accurately detect, identify, and track people within opportune and calm home environment to be used as infrastructure for various application domains such as assisted living, healthcare, security and energy management. Accordingly, a novel multimodal approach for non-tagged human identification and tracking within home environment is proposed. The proposed approach combined floor pressure and PIR sensors through unique designed integration strategy aiming to merge the advantages of the two sensor types and overcome or minimise their weaknesses. The designed strategy enabled the PIR output signal pattern to afford explicit information indicating a person’s body surface area (size/shape). This information enhanced the identification accuracy, facilitated the custom designed smart floor, and reduced the overall cost. The conceptual framework of the proposed approach/strategy encompassed two key stages, hardware system design and implementation, and data processing. The hardware system design included the custom designed PIR and smart floor units. A test bed was designed and implemented for supporting the research studies, including proof of concept, concept demonstration, experimental and test cases studies. Data processing system has divided into different stages to accomplish the identification and tracking goals. First, the interested patterns were segmented and generated with threshold edge detection method and advanced pattern generation algorithm respectively. Second, limited set of features were extracted and selected from each pattern including ground reaction force GRF, gait, and body size/shape (PIR) features. Third, these features were merged at different fusion level, namely, feature-level and decision-level to provide comprehensive description about the person’s identity. Fourth, MLPNN multiclass classifier was adopted to process the feature vectors and recognise the person’s identity. Finally, the footstep patterns were tracked using weighted centroid tracking technique, in addition to MLPNN classifier to handle the footsteps association problems. Four test cases were designed and carried out to demonstrate, test, and evaluate the feasibility and effectiveness of the proposed non-tagged identification and tracking strategies/approach. The assessment outcomes have shown the potential of the proposed multimodal approach as an advanced strategy for implementation of an indoor non-tagged human identification and tracking system and to be used as infrastructure for supporting the delivery of various types of smart services within the smart home environments. In summary, the proposed multimodal approach has the potential to: (1) Identify up to 5 persons successfully with minimum 98.8% correct classification rate without tag, (2) detect, locate, and track multiple persons successfully without tag and the location error no more than 11.76 cm, approximately 1.5 times better in accuracy than the original set target (i.e. 30 cm), and (3) able to handle various tracking difficulties and solve 97.5% of data association problems.
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