Daily Human Motion Research Articles

Ferroelectric composite-based piezoelectric energy harvester for self-powered detection of obstructive sleep

Lead-free piezoelectric ceramic is a promising material for energy harvesters, as they have superior electromechanical, ferroelectric, and piezoelectric properties. In addition, piezoelectric ceramics can be blended with polymer to achieve high-flexibility polymer-ceramic composites, providing mechanical robustness and stability. In this context, a new lead-free ferroelectric material, having the chemical formula SrTi2O5 (STO), was synthesized using a high-temperature solid-state reaction. Detailed analyses of the structural, morphological, and electrical properties of the synthesized material were performed. STO crystallizes with orthorhombic symmetry and space group of Cmm2. The frequency and temperature-dependent dielectric parameters were evaluated, and impedance spectroscopy shed light on the charge dynamics. The PDMS-STO composites at different mass fraction of the STO were prepared using a solvent casting route, and a corresponding piezoelectric nanogenerator (PENG) was developed. The electrical output of the different PENG by varying massfractions of STO in PDMS and varying force were investigated. The 15% (in mass) PENG device delivered the highest peak-to-peak voltage, current, and power density of 25 V, 92 nA, and 0.64 μW @ 500 MΩ, respectively. The biomechanical energy harvesting using the PENG device by daily human motions, bending of the device, and attaching the device to laboratory equipment was demonstrated. Later the PENG device was attached to the human throat region, and snoring signals were recorded. A classification model was designed employing the convolutional neural network (CNN) model. Efforts have been laid to differentiate between normal and abnormal snores, which could help the patient with screening and early disease detection, contributing to self-powered healthcare applications.

Human Motion Recognition With Spatial-Temporal-ConvLSTM Network Using Dynamic Range-Doppler Frames Based on Portable FMCW Radar

As a noncontact and noninvasive sensor, radar offers attractive modality for human motion recognition in many fields, such as surveillance, smart homes, and assisted living. As a dynamic process, a comprehensive multidomain analysis in stream is crucial for human motion recognition. In this article, a novel spatial–temporal convolution long short-term memory (ST-ConvLSTM) network using dynamic range-Doppler frames (DRDF) based on portable frequency-modulated continuous-wave (FMCW) radar is proposed to address this problem. First, DRDF is introduced to characterize different human motions in time, range, Doppler, and radar cross section (RCS) domains with a time-sequenced range-Doppler maps. Then, ConvLSTM cell is investigated to extract continuous 2-D image features by combining both convolution operation and LSTM cell. Furthermore, a spatial attention module is utilized to emphasize the spatial distribution of human motion in the range-Doppler domain, while a temporal attention module is designed to obtain optimal temporal weights considering behavior continuity. Finally, an extensive radar dataset from 16 volunteers demonstrated its feasibility and superiority in the classification of six typical daily human motions. The contributions of attention modules and its robustness facing individual diversity are also investigated and discussed.

Scavenging Mechanical Energy from Human Motions Using Novel‐Biomaterial‐Based Triboelectric Nanogenerator

In today's world, people are surrounded by smart wireless devices which require sustainable and eco‐friendly power source. Triboelectric nanogenerator (TENG) is emerging as an imperative source to produce clean, cost‐effective, and easily fabricated battery‐less devices. The emphasis is on using biomaterials to play a vital role for the fabrication of such self‐ powered system. Herein, an approach is discussed to fabricate a novel TENG from the waste biomaterials (garlic tunic, onion tunic, and almond peel) which successfully harvests the electrical energy from daily human motions (walking and running). Egg shell membrane–polytetrafluoroethylene (ESM–PTFE) TENG generates 56.6 V/0.53 μA and is closely followed by garlic tunic–PTFE TENG with 44.6 V/0.36 μA, onion tunic–PTFE TENG with 41.4 V/0.32 μA, and almond peel–PTFE TENG with 40.2 V/0.29 μA, respectively. Moreover, ESM‐based TENG performance is examined using all other biomaterial combinations. These TENGs produce sufficient energy that can power an array of tens of green light‐emitting diodes (LEDs). Finally, the power from human activities is harvested to control small portable electronic devices.

Flexible and porous microneedles of PDMS for continuous glucose monitoring.

Microneedle (MN) is a key technology of the biomedical engineering field due to its capability of accessing the biological information in a minimally invasive manner. One of the huge demands for next-generation healthcare monitoring is continuous monitoring, especially of blood glucose concentration. For this, MN should be kept inserted into the human skin for a certain period of time, enduring stresses induced by daily human motion and at the same time measuring biomarkers in ISF. However, conventional MNs for biosensing are not suitable for a long term insertion due to the rigid structure and biological risks of MN breakage. In this study, a novel MN structure is proposed and investigated by combining flexible "sponge-like" porous PDMS matrix and coating by biodissolving hyaluronic acid (HA). The fabricated porous MNs coated with HA show ideal mechanical characteristics, by which the MNs are rigid enough to penetrate the skin and become flexible after insertion into the skin. It is also shown that the MN array successfully extracts ISF in vitro and in vivo not by capillary action but by repeated compressions. The results show the applicability of the flexible MNs to continuous blood glucose monitoring.

Multi-Classification Algorithm for Human Motion Recognition Based on IR-UWB Radar

In this paper, a multi-classification algorithm for human motion recognition based on Impulse-Radio Ultra-wideband (IR-UWB) radar is presented. The algorithm includes three parts. First, the k-NearestNeighbor (KNN) algorithm is used to classify the radial features of pre-processed signal to determine the subject’s radial displacement direction. Then, the power spectrum feature extraction algorithm and Doppler shifts feature extraction algorithm are proposed to extract and visualize the characteristics from the different categories classified by the first part. Finally, the feature spectrograms obtained by the second part are sent into Convolutional Neural Networks (CNNs) for training and testing to realize the recognition of human motions. To verify the performance of proposed algorithm, dataset was created from 15 persons including 12 kinds of motions. The Five-Fold Cross Validation was conducted to calculate the recognition accuracy. As a result, the average accuracy of judging the radial displacement directions of subjects was up to 99%. Furthermore, the average accuracy of estimating the motions of subjects reached 98%. Experiments have proved that the proposed algorithm can achieve high recognition accuracy in daily human motions and is feasible in a variety of test environments.

Exo-shoe triboelectric nanogenerator: Toward high-performance wearable biomechanical energy harvester

Abstract Among various daily human motions, walking is considered the most natural and strongest one. In this study, the exo-shoe TENG (ES-TENG) with the rationally designed bi-directional gearbox is developed to generate extended and enhanced output performance during daily walking. This bi-directional gearbox effectively converts the whole stroke of a reciprocating and low frequency (1 Hz) natural stepping motion into uni-directional high-speed rotation (700 rpm). The proposed ES-TENG exhibits more than ten times enhancement of the output performance with effective motion transformation. Although the normal stepping motion is not interrupted by the ES-TENG, the specific power of the ES-TENG could reach up to 13 µW/g through a single daily stepping motion. Real-time self-powered environmental monitoring and charging wearable electronics are demonstrated as proof-of-concept applications. Furthermore, the voltage multiplier circuit is used to generate a boosted open-circuit voltage of more than 3 kV, which shows the potential electric treatment applications of the ES-TENG. The ES-TENG provides the potential of biomechanical energy harvesting with a broad range of applications through the generation of highly enhanced output performance under natural daily stepping motions.

An effective self-powered strategy to endow titanium implant surface with associated activity of anti-biofilm and osteogenesis

Abstract The implant-associated infections will cause implant failure, which bring patient suffering and risk of infection death. Once forming bacteria biofilms on implant, the resistance to antibiotics will increase hundreds of times. However, the recent methods that endow implant with antibacterial activity, are always time-limited, process complicated, unstable, introducing new antibacterial components with side effects and showing little effect on mature bacterial biofilm. Meanwhile, some method may inhibit osteoblasts’ adhesion and suppress osteogenesis. Here, we present a self-powered method of loading and accumulating negative charges on the surface of anodized titanium implant by using a triboelectric nanogenerator (TENG). The most obvious advantage is that TENG can harvest and transfer mechanical energy from daily human motions to electrical energy for building the stable and long-term effective negatively charged implant surface, which effectively inhibits bacterial adhesion, reduces bacteria number and lowers the live/dead bacteria ratio in forming and mature biofilms. Moreover, the treated implant reveals osteogenesis promotion in MC3T3-E1 cells, without adverse side effects. Our findings provide a promising method for energizing implant with anti-biofilm and osteogenesis promotion activity by self-powered technology in vivo, which may also encourage new design impetus for multi-functional material as implant and for medical devices in future.

Structural and electrochemical properties of LiMn0.6Fe0.4PO4 as a cathode material for flexible lithium-ion batteries and self-charging power pack

Cathode materials with low-cost, environment-friendly, high energy density are critical for lithium-ion batteries (LIBs). Here, the effects of Fe doping on the structure of LiMnPO4 (LMP) are investigated by neutron powder diffraction (NPD). The prepared LiMn0.6Fe0.4PO4/carbon (LMFP/C) shows a higher specific capacity of 90 mAh g-1 at a current density of 1 C, which is about 5 times of that of LiMnPO4/C. It also shows excellent cycling performance for 1000 cycles. The improved electrochemical performance is ascribed to the higher octahedral distortion of (Mn, Fe)O6 and an easiness for Li diffusion due to much less anisotropic ellipsoids for Li in LMFP. We further fabricated a flexible LIB with LMFP/C cathode and an in-situ polymerized electrolyte, which exhibits excellent flexibility and cyclability. The cell shows no obvious performance degradation after bending for 300 times. Moreover, a flexible triboelectric nanogenerator (TENG) was coupled with the flexible cell to form a wearable self-charging power pack. The TENG can harvest mechanical energy and convert it into electrical form, charge the battery to supply energy for a flexible electrochromic membrane. The open circuit voltage (VOC) of the flexible LIB increases from 3.32 V to 3.51 V in about 20 min through daily human motion.

Classification of Multi-class Daily Human Motion using Discriminative Body Parts and Sentence Descriptions

In this paper, we propose a motion model that focuses on the discriminative parts of the human body related to target motions to classify human motions into specific categories, and apply this model to multi-class daily motion classifications. We extend this model to a motion recognition system which generates multiple sentences associated with human motions. The motion model is evaluated with the following four datasets acquired by a Kinect sensor or multiple infrared cameras in a motion capture studio: UCF-kinect; UT-kinect; HDM05-mocap; and YNL-mocap. We also evaluate the sentences generated from the dataset of motion and language pairs. The experimental results indicate that the motion model improves classification accuracy and our approach is better than other state-of-the-art methods for specific datasets, including human–object interactions with variations in the duration of motions, such as daily human motions. We achieve a classification rate of 81.1% for multi-class daily motion classifications in a non cross-subject setting. Additionally, the sentences generated by the motion recognition system are semantically and syntactically appropriate for the description of the target motion, which may lead to human–robot interaction using natural language.

Helical Piezoelectric Energy Harvester and Its Application to Energy Harvesting Garments

In this paper, we propose a helical piezoelectric energy harvester, examine its application to clothes in the form of an energy harvesting garment, and analyze its design and characteristics. The helical harvester is composed of an elastic core and a polymer piezoelectric strap twining the core. The fabricated harvester is highly elastic and can be stretched up to 158% of its initial length. Following the experiments using three different designs, the maximum output power is measured as 1.42 mW at a 3 MΩ load resistance and 1 Hz motional frequency. The proposed helical harvesters are applied at four positions of stretchable tight-fitting sportswear, namely shoulder, arm joint, knee, and hip. The maximum output voltage is measured as more than 20 V from the harvester at the knee position during intended body motions. In addition, electric power is also generated from this energy harvesting garment during daily human motions, which is about 3.9 V at the elbow, 3.1 V at the knee, and 4.4 V at the knee during push-up, walking, and squatting motions, respectively.

A self-charging power unit by integration of a textile triboelectric nanogenerator and a flexible lithium-ion battery for wearable electronics.

A novel integrated power unit realizes both energy harvesting and energy storage by a textile triboelectric nanogenerator (TENG)-cloth and a flexible lithium-ion battery (LIB) belt, respectively. The mechanical energy of daily human motion is converted into electricity by the TENG-cloth, sustaining the energy of the LIB belt to power wearable smart electronics.