An implantable stacked planar inverted-F antenna (PIFA) for biotelemetric communication in the 402–405 MHz Medical Implant Communications Service (MICS) frequency band is designed and fabricated. With the proposed PIFA structure, a slot on each radiating patch was embedded, resulting in a size reduction of 0.013 λ and a compact size of 10 × 10 × 1.905 mm3. Both in vitro and in vivo experiments verified the simulation performance with characteristics of −10 dB bandwidth of 29 MHz, radiation efficiency of 0.9%, and a maximum far-field gain of −18.8 dB. We calculated the safety power delivered to the antenna using the specific absorption rate (SAR) limitation standard. Compared to other implantable antennas for biotelemetry, this antenna performs comparably and has a smaller size. This design would further develop implantable medical devices that communicate in the MICS band.

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Background: With the rapid increase of stroke incidence in recent years worldwide, home-based rehabilitation training has become more needed, especially for remote regions or in developing countries where rehabilitation resources are scarce. Studies have demonstrated that home-based rehabilitation for poststroke patients is essential for reducing the cost as well as for providing efficient rehabilitation. Nevertheless, home-based rehabilitation training requires effective professional support and timely evaluation. Method: In this paper, a home-based rehabilitation quality evaluation method for lower limb training was proposed. The kinematic data of a patient’s lower limb during a set of selected training exercises was captured by a wireless body area sensor network (WBASN). The data was then processed by a convolutional neural network (CNN) based algorithm to classify the rehabilitation training type and to evaluate the training quality. A series of kinematic features were selected for rehabilitation quality scoring. The experiments have been conducted using 26 human participants, including 6 healthy participants and 20 stroke patients at different Brunnstrom recovery stages. Results: An accuracy of 95.3% has been achieved for recognizing the rehabilitation training types and a statistically significant positive correlation has been obtained between the objective scores and the Brunnstrom stages evaluated by the clinicians.

Background: With the rapid increase of stroke incidence in recent yearsworldwide, home-based rehabilitation training has become more needed,especially for remote regions or in developing countries where rehabilitationresources are scarce. Studies have demonstrated that home-based rehabilitationfor poststroke patients is essential for reducing the cost as well as for providingefficient rehabilitation. Nevertheless, home-based rehabilitation training requireseffective professional support and timely evaluation. Method: In this paper, a home-based rehabilitation quality evaluation methodfor lower limb training was proposed. The kinematic data of a patient’s lowerlimb during a set of selected training exercises was captured by a wireless bodyarea sensor network (WBASN). The data was then processed by a convolutionalneural network (CNN) based algorithm to classify the rehabilitation training typeand to evaluate the training quality. A series of kinematic features were selectedfor rehabilitation quality scoring. The experiments have been conducted using 26human participants, including 6 healthy participants and 20 stroke patients atdifferent Brunnstrom recovery stages. Results: An accuracy of 95.3% has been achieved for recognizing therehabilitation training types and a statistically significant linear positivecorrelation (R2 = 0.9962) has been obtained between the objective scores andthe Brunnstrom stages evaluated by the clinicians.