Thatte Gautam, Li Ming, Emken Adar, Mitra Urbashi, Narayanan Shri, Annavaram Murali, Spruijt-Metz Donna
Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, CA 90089, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:4678-81. doi: 10.1109/IEMBS.2009.5334222.
Multi-hypothesis activity-detection using a wireless body area network is considered. A fusion center receives samples of biometric signals from heterogeneous sensors. Due to the different discrimination capabilities of each sensor, an optimized allocation of samples per sensor results in lower energy consumption. Optimal sample allocation is determined by minimizing the probability of misclassification given the current activity state of the user. For a particular scenario, optimal allocation can achieve the same accuracy (97%) as equal allocation across sensors with an energy savings of 26%. As the number of samples is an integer, further energy reduction is achieved by developing an approximation to the probability of misclassification which allows for a continuous-valued vector optimization. This alternate optimization yields approximately optimal allocations with significantly lower complexity, facilitating real-time implementation.
考虑使用无线体域网进行多假设活动检测。融合中心从异构传感器接收生物特征信号样本。由于每个传感器的辨别能力不同，每个传感器的样本优化分配可降低能耗。给定用户当前的活动状态，通过最小化误分类概率来确定最优样本分配。对于特定场景，最优分配可实现与传感器间均等分配相同的准确率（97%），同时节省26%的能量。由于样本数量为整数，通过对误分类概率进行近似处理实现了进一步的能量降低，这允许进行连续值向量优化。这种交替优化产生了复杂度显著更低的近似最优分配，便于实时实现。
