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The vast proliferation of wearables and smart sensing devices in the last decade has created an immense demand for new and efficient powering solutions. The research focus has shifted towards developing simple, cost-effective, flexible device topologies capable of capturing kinetic energy associated with the human body. Piezoelectric and Triboelectric mechanisms are widely employed to convert biomechanical energy to electrical power due to their inherent merits in terms of affordable designs and high energy conversion efficiencies. In this work, we propose a flexible hybrid generator topology incorporating both piezoelectric and triboelectric mechanisms to achieve high electrical output from human motion. To enhance the efficiency and obtain a symmetric output, dual triboelectric generators are employed, which generate time-multiplexed output across the same set of electrodes. The device displays a characteristic ability to distinguish between different body movements as its output depends on the contact area as well as the pressure generated by the motion. This creates numerous avenues for employing the device in self-powered tactile sensing applications. The unique single substrate design makes the device robust and increases its longevity. The V-shaped prototype having an active area of 3.5 cm × 2 cm, is tested under a wide range of biomechanical stimuli, including touching, tapping, and pressing motions. The practical applications of the proposed device as an add-on patch on fabrics, as an in-sole device, and for powering commercial electronics are demonstrated. Apart from this, the reported generator can also fuel low-power devices from various other day-to-day human activities.

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In this work, we present a highly effective and scalable design strategy of a triboelectric-piezoelectric hybrid array of three cantilever beams stacked over each other (wideband operation regime), which can also be rotated around their mean position to vibrate freely without impacting any other layer (narrowband operation regime). Contrary to a unique frequency response exhibited by conventional devices, the proposed device can switch between narrowband and wideband frequency responses around different central frequencies. This work elaborately discusses the frequency response of mechanical stopper-based PEG and TEGs at varying gap lengths, excitations, and resonant frequencies, and the design of the hybrid array is optimized based on it. The performance of this device is characterized using simulation analysis and experimental validation. Experimentally, the device generates net power greater than 0.3 µW (Piezoelectric) and 0.4 µW (Triboelectric) continually between the frequencies of 30 to 60 Hz in the wideband operation regime and output power of 0.81 µW, 0.65 µW, and 0.62 µW at 18, 27, and 36 Hz in the narrowband operation regime under mechanical excitation of 0.75g. The remarkable performance of the device at different frequency ranges demonstrates its potential in various harvesting and sensing applications.

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The sustenance of the growing Internet of Things revolution requires suitable self‐powering solutions, which can be appropriately complemented by developing efficient energy harvesting systems. Typically, conventional piezoelectric thin film‐based energy harvesters are not promising due to high cost, low coverage area, and size‐frequency‐power trade‐off. Piezoelectric/triboelectric transduction driven mechanical energy harvesters (MEHs) based on nanocomposites offer better efficiency, cost‐effectiveness, and large‐scale production. Here, % weight ratio‐dependent piezoelectric/triboelectric property analysis, and optimization of Barium Titanate (BTO)/SU‐8 based photopatternable nanocomposite thin films are reported for developing highly efficient MEHs. Further, the performance of the nanocomposite is shown to be enhanced by controlled graphene nano‐platelet doping and ultraviolet (UV) exposure. Elaborate Finite Element Method (FEM) study is performed to support the experimental findings. Finally, three MEH devices are developed based on the optimally prepared variants of the nanocomposite and compared experimentally. A maximum output voltage of ≈3 V and power density of 0.65 µW cm−2 are obtained at 0.75 g and at the resonance frequency of 38 Hz from the graphene doped 20% BTO/SU‐8 based harvester. The prototypes have demonstrated the potential to deliver a regulated output voltage of 3.3 V within 40 s of periodic excitation upon integration with a customized power management unit for powering low‐power electronics.

Brain Computer Interface systems are the tools that have been put forward to help the disabled people who are inadequate of retorting to a computer using brain signal. The development in home automation is moving forward towards the future in creating the ideal smart homes environment. Optionally, home automation system design also been develop for certain situation which for those who need a special attention such as old age person, sick patients, and handicapped person. A braincomputer interface (BCI), often called a mind-machine interface (MMI), or sometimes called a brain-machine interface (BMI), it is a direct communication pathway between the brain and an external device. Most research investigating BCI in humans has used scalp-recorded electroencephalography or intracranial electrocorticography. The use of brain signals obtained directly from stereotactic depth electrodes to control a BCI has not previously been explored. In this paper, we present a smart home automation system using brain-computer interface. BCI is becoming progressively studied as the way users interact with computers because recent technological developments have led to low priced, high exactness BCI devices. These systems that can detour conventional channels of communication (i.e., muscles and thoughts) between human brain and corporeal devices, to provide undeviating communication and sway, by recasting different patterns of brain activity into commands in real time. The device tested in this paper is called Neurosky Necomimmi Brainwave Cat Ears, which is an electroencephalograph (EEG) measuring device and enables the measuring of brain bustle. With the help of this, we are analyzing the brain wave signals. Human brain has millions of interconnected neurons. The interaction pattern between these neurons is represented as emotional states and thoughts. This pattern will be changing as the human thoughts change, which in turn produces different electrical waves. These electrical waves will be sensed by the brain wave sensor and it will convert the data into packets and transmit via Bluetooth medium. Keywords: Brain Computer Interface (BCI), Neurons, Brainwave sensor, Electroencephalogram (EEG), Neurosky, and Arduino. I.INTRODUCTION BCI is used to communicate based on human brains neural activity and it is very much independent of output generated by peripheral nerves and muscles. Typical BCI equipment that utilizes EEG to measure brain activity is expensive and requires expert knowledge to setup and use. The signals generated by brain are received by the brain sensor and are divided into packets and the packet data is transmitted to wireless medium (Bluetooth). The wave measuring unit will receive the brain wave in raw data format and it will convert it into signal. Brain-computer interface is nothing but the interaction between the human brain system and machines. It is a control system which enables the people to communicate and control a device by mere thinking. BCI collects the information from the brain and give command...