In this study, the authors present a simple reconfigurable multiband antenna with two PIN diode switches for worldwide interoperability for microwave access (WiMAX)/wireless local area networks (WLAN) applications. The antenna permits reconfigurable switching in up to ten frequency bands between 2.2 and 6 GHz, with relative impedance bandwidths of around 2.5 and 8%. The proposed antenna has been simulated using computer simulation technology microwave studio software and fabricated on an FR‐4 substrate. It is compact, with an area of 50 × 45 mm ² , and has a slotted ground substrate. Both measured and simulated return loss characteristics of the optimised antenna show that it satisfies the requirement of 2.4/5.8 GHz WLAN and 3.5 GHz WiMAX antenna applications. Moreover, there is good agreement between the measured and simulated results in terms of radiation pattern and gain.

A miniaturized textile antenna is designed and demonstrated for ISM band applications at 2.4 GHz. The proposed antenna uses appropriate loading of a rectangular slot/notch with a strip line inserted to form an inverted E-shaped antenna. The structure is simple, compact, and easy to manufacture using only fabric material. The antenna size is 75% smaller than a conventional antenna. When the antenna is subjected to bending, its performance proves sustainable under deformation. Each slot/notch and strip line is translated to its equivalent circuit and then integrated to form the whole equivalent circuit of the proposed antenna. The results from the equivalent circuits and simulation show acceptable agreement. The antenna shows an improvement of size miniaturization of 30×20×0.7 mm ³ and demonstrates an impedance bandwidth of 15% and efficiency of 79%, showing that it is a promising candidate for incorporation into wearable systems.

Human activity recognition from sensor readings has proved to be an effective approach in pervasive computing for smart healthcare. Recent approaches in ambient assisted living (AAL) within the home or community setting offers people the prospect of independent care and improved quality of living. However, most of the available AAL systems are limited by several factors including system complexity and computational cost. In this paper, a simple, novel ambient human activity recognition framework using the multivariate Gaussian is proposed. The classification framework augments prior information from passive RFID tags to obtain more detailed activity profiling. The proposed algorithm based on multivariate Gaussian via maximum likelihood estimation is used to learn the features of the human activity model. Twelve sequential and concurrent experimental evaluations are conducted in a mock apartment environment. The sampled activities are predicted using a new dataset of the same activity and high prediction accuracy established. The proposed framework suits well for the single and multi-dwelling environment and offers pervasive sensing environment for elderly, disabled and carers.

Human activity recognition from sensor data is a critical research topic to achieve remote health monitoring and ambient-assisted living (AAL). In AAL, sensors are integrated into conventional objects aimed to support targets capabilities through digital environments that are sensitive, responsive, and adaptive to human activities. Emerging technological paradigms to support AAL within the home or community setting offers people the prospect of a more individually focused care and improved quality of living. In the present work, an ambient human activity classification framework that augments information from the received signal strength indicator (RSSI) of passive RFID tags to obtain detailed activity profiling is proposed. Key indices of position, orientation, mobility, and degree of activities which are critical to guide reliable clinical management decisions using four volunteers are employed to simulate the research objective. A two-layer, fully connected sequence long short-term memory recurrent neural network model (LSTM RNN) is employed. The LSTM RNN model extracts the feature of RSS from the sensor data and classifies the sampled activities using SoftMax. The performance of the LSTM model is evaluated for different data size and the hyperparameters of the RNN are adjusted to optimal states, which results in an accuracy of 98.18%. The proposed framework suits well for smart health and smart homes which offers a pervasive sensing environment for the elderly, persons with disability and chronic illness.