Falls are the primary cause of accidents for the elderly in the living environment. Reducing hazards in the living environment and performing exercises for training balance and muscles are the common strategies for fall prevention. However, falls cannot be avoided completely; fall detection provides an alarm that can decrease injuries or death caused by the lack of rescue. The automatic fall detection system has opportunities to provide real-time emergency alarms for improving the safety and quality of home healthcare services. Two common technical challenges are also tackled in order to provide a reliable fall detection algorithm, including variability and ambiguity. We propose a novel hierarchical fall detection algorithm involving threshold-based and knowledge-based approaches to detect a fall event. The threshold-based approach efficiently supports the detection and identification of fall events from continuous sensor data. A multiphase fall model is utilized, including free fall, impact, and rest phases for the knowledge-based approach, which identifies fall events and has the potential to deal with the aforementioned technical challenges of a fall detection system. Seven kinds of falls and seven types of daily activities arranged in an experiment are used to explore the performance of the proposed fall detection algorithm. The overall performances of the sensitivity, specificity, precision, and accuracy using a knowledge-based algorithm are 99.79%, 98.74%, 99.05% and 99.33%, respectively. The results show that the proposed novel hierarchical fall detection algorithm can cope with the variability and ambiguity of the technical challenges and fulfill the reliability, adaptability, and flexibility requirements of an automatic fall detection system with respect to the individual differences.

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Falls are the primary cause of accidents among the elderly and frequently cause fatal and non-fatal injuries associated with a large amount of medical costs. Fall detection using wearable wireless sensor nodes has the potential of improving elderly telecare. This investigation proposes a ZigBee-based location-aware fall detection system for elderly telecare that provides an unobstructed communication between the elderly and caregivers when falls happen. The system is based on ZigBee-based sensor networks, and the sensor node consists of a motherboard with a tri-axial accelerometer and a ZigBee module. A wireless sensor node worn on the waist continuously detects fall events and starts an indoor positioning engine as soon as a fall happens. In the fall detection scheme, this study proposes a three-phase threshold-based fall detection algorithm to detect critical and normal falls. The fall alarm can be canceled by pressing and holding the emergency fall button only when a normal fall is detected. On the other hand, there are three phases in the indoor positioning engine: path loss survey phase, Received Signal Strength Indicator (RSSI) collection phase and location calculation phase. Finally, the location of the faller will be calculated by a k-nearest neighbor algorithm with weighted RSSI. The experimental results demonstrate that the fall detection algorithm achieves 95.63% sensitivity, 73.5% specificity, 88.62% accuracy and 88.6% precision. Furthermore, the average error distance for indoor positioning is 1.15 ± 0.54 m. The proposed system successfully delivers critical information to remote telecare providers who can then immediately help a fallen person.

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Iridium oxide (IrOx) is a promising implantable electrode material owing to its remarkable neural stimulation capacity. However, presently, IrOx electrodes lack biocompatibility and bioactive interactions with nerve tissues. Application of polymeric surface coatings results in a weak physical adhesion at the organic/inorganic interface, which limits their wide‐scale application. Herein, a smart iridium oxide‐plasma protein (IrOx‐PP) electrode with enhanced electroactivity, electrochemical stability, cytocompatibility, and bioactivity that can provide controllable topographical, electrical, and chemical stimuli to enhance neuronal activity is proposed. In the inorganic/organic nanoparticle (NP)‐protein corona structures, the soft NP‐corona led to repeated burst‐to‐zero‐to‐burst PP release, while the hard NP‐corona with an ordered atomic structure enhanced the electrochemical stability and bioactivity. The incorporated PP resulted in a higher current storage capacity, lower impedance, better cell growth, and significant neurite outgrowth compared with those obtained with pristine IrOx. The application of electrical stimulation to IrOx‐PP enabled simultaneous neuromodulation, on‐demand PP release, and cell uptake, with a 2‐fold higher cell density and significant neurite outgrowth on IrOx‐PP than on pristine IrOx. This bioactive inorganic‐organic hybrid electrode with the combined features of physical properties and improved neuromodulation is expected to be a revolutionary platform for efficient and biocompatible neural implantation.