Despite the importance of cardiorespiratory fitness, no practical method exists to estimate maximal oxygen consumption (VO₂max) without a specific exercise protocol. We developed an estimation model of VO₂max, using maximal activity energy expenditure (aEEmax) as a new feature to represent the level of physical activity. Electrocardiogram (ECG) and acceleration data were recorded for 4 days in 24 healthy young men, and reference VO₂max levels were measured using the maximal exercise test. aEE was calculated using the measured acceleration data and body weight, while heart rate (HR) was extracted from the ECG signal. aEEmax was obtained using linear regression, with aEE and HR as input parameters. The VO₂max was estimated from the aEEmax using multiple linear regression modeling in the training group (n = 16) and was verified in the test group (n = 8). High correlations between the estimated VO₂max and the measured VO₂max were identified in both groups, with a 15-hour recording being sufficient to produce a highly accurate VO₂max estimate. Additional recording time did not significantly improve the accuracy of the estimation. Our VO₂max estimation method provides a robust alternative to traditional approaches while only requiring minimal data acquisition time in daily life.
Acceleration ; Body Weight ; Electrocardiography ; Energy Metabolism ; Exercise Test ; Heart Rate ; Humans ; Linear Models ; Male ; Methods ; Motor Activity ; Oxygen Consumption

OBJECTIVE: To compare optical motion capture system (MoCap), attitude and heading reference system (AHRS) sensor, and Microsoft Kinect for the continuous measurement of cervical range of motion (ROM). METHODS: Fifteen healthy adult subjects were asked to sit in front of the Kinect camera with optical markers and AHRS sensors attached to the body in a room equipped with optical motion capture camera. Subjects were instructed to independently perform axial rotation followed by flexion/extension and lateral bending. Each movement was repeated 5 times while being measured simultaneously with 3 devices. Using the MoCap system as the gold standard, the validity of AHRS and Kinect for measurement of cervical ROM was assessed by calculating correlation coefficient and Bland–Altman plot with 95% limits of agreement (LoA). RESULTS: MoCap and ARHS showed fair agreement (95% LoA<10°), while MoCap and Kinect showed less favorable agreement (95% LoA>10°) for measuring ROM in all directions. Intraclass correlation coefficient (ICC) values between MoCap and AHRS in –40° to 40° range were excellent for flexion/extension and lateral bending (ICC>0.9). ICC values were also fair for axial rotation (ICC>0.8). ICC values between MoCap and Kinect system in –40° to 40° range were fair for all motions. CONCLUSION: Our study showed feasibility of using AHRS to measure cervical ROM during continuous motion with an acceptable range of error. AHRS and Kinect system can also be used for continuous monitoring of flexion/extension and lateral bending in ordinary range.
Adult ; Head* ; Humans ; Neck ; Range of Motion, Articular*