比较不同位置加速度计对日常活动能耗的预测效果，发现骨盆+大腿组合最准。
Accelerometry-based Energy Expenditure Estimation During Activities of Daily Living: A Comparison Among Different Accelerometer Compositions
- 用骨盆和大腿加速度计组合，比手腕更准确预测能耗
- 骨盆+两腿组合的模型决定系数达0.53，手腕接近0
- 双腕表现无差异，但均远不如躯干部位数据
体力活动能量消耗（PAEE）可通过呼吸代谢数据作为参考基准，也可通过身体运动信号估算。本研究以COSMED K5测量的呼吸数据为参考，对比了基于躯干中心质量（COM）与腕部加速度计的预测性能。实验采用9名参与者在进行日常生活活动时佩戴5个加速度计（骨盆、双大腿、双腕）的数据集，应用线性回归（LR）与CNN-LSTM两种模型。结果显示，骨盆+双大腿三传感器组合（3-acc）在两种模型中均表现最佳：LR模型$R^2=0.41$，CNN-LSTM模型$R^2=0.53$；骨盆单传感器（pelvis-acc）与3-acc组合无显著差异（p=0.278）。而左腕（l-wrist-acc）与右腕（r-wrist-acc）设置的$R^2$均接近0，显著低于躯干组（p<0.05），且两腕之间无差异（p=0.329）。
原文摘要 · Abstract (English)
Physical activity energy expenditure (PAEE) can be measured from breath-by-breath respiratory data, which can serve as a reference. Alternatively, PAEE can be predicted from the body movements, which can be measured and estimated with accelerometers. The body center of mass (COM) acceleration reflects the movements of the whole body and thus serves as a good predictor for PAEE. However, the wrist has also become a popular location due to recent advancements in wrist-worn devices. Therefore, in this work, using the respiratory data measured by COSMED K5 as the reference, we evaluated and compared the performances of COM-based settings and wrist-based settings. The COM-based settings include two different accelerometer compositions, using only the pelvis accelerometer (pelvis-acc) and the pelvis accelerometer with two accelerometers from two thighs (3-acc). The wrist-based settings include using only the left wrist accelerometer (l-wrist-acc) and only the right wrist accelerometer (r-wrist-acc). We implemented two existing PAEE estimation methods on our collected dataset, where 9 participants performed activities of daily living while wearing 5 accelerometers (i.e., pelvis, two thighs, and two wrists). These two methods include a linear regression (LR) model and a CNN-LSTM model. Both models yielded the best results with the COM-based 3-acc setting (LR: $R^2$ = 0.41, CNN-LSTM: $R^2$ = 0.53). No significant difference was found between the 3-acc and pelvis-acc settings (p-value = 0.278). For both models, neither the l-wrist-acc nor the r-wrist-acc settings demonstrated predictive power on PAEE with $R^2$ values close to 0, significantly outperformed by the two COM-based settings (p-values $<$ 0.05). No significant difference was found between the two wrists (p-value = 0.329).
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