The optimal allocation of samples for physical activity detection in a wireless body area network for health-monitoring is considered. The number of biometric samples collected at the mobile device fusion center, from both device-internal and external Bluetooth heterogeneous sensors, is optimized to minimize the transmission power for a fixed number of samples, and to meet a performance requirement defined using the probability of misclassification between multiple hypotheses. A filter-based feature selection method determines an optimal feature set for classification, and a correlated Gaussian model is considered. Using experimental data from overweight adolescent subjects, it is found that allocating a greater proportion of samples to sensors which better discriminate between certain activity levels can result in either a lower probability of error or energy-savings ranging from 18% to 22%, in comparison to equal allocation of samples. The current activity of the subjects and the performance requirements do not significantly affect the optimal allocation, but employing personalized models results in improved energy-efficiency. As the number of samples is an integer, an exhaustive search to determine the optimal allocation is typical, but computationally expensive. To this end, an alternate, continuous-valued vector optimization is derived which yields approximately optimal allocations and can be implemented on the mobile fusion center due to its significantly lower complexity.

中文翻译：

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节能体力活动检测的最佳时间资源分配


考虑了用于健康监测的无线体域网中身体活动检测的样本的最佳分配。在移动设备融合中心从设备内部和外部蓝牙异构传感器收集的生物特征样本数量经过优化，以最小化固定数量样本的传输功率，并满足使用错误分类概率定义的性能要求多个假设之间。基于滤波器的特征选择方法确定用于分类的最佳特征集，并考虑相关高斯模型。使用超重青少年受试者的实验数据发现，相比之下，将更大比例的样本分配给能够更好地区分某些活动水平的传感器，可以降低出错概率，或者节省 18% 至 22% 的能源。样本的均等分配。受试者当前的活动和性能要求不会显着影响最优分配，但采用个性化模型可以提高能源效率。由于样本数量是整数，因此通常需要进行详尽的搜索来确定最佳分配，但计算成本较高。为此，导出了一种替代的连续值矢量优化，它产生近似最优的分配，并且由于其复杂性显着降低，因此可以在移动融合中心上实现。