## 1 Introduction  
Human movement analysis has traditionally been the domain of the laboratory, where optoelectronic systems have been the gold standard [1; 2]. Their use remains the cornerstone of practice in biomechanics and kinesiology. However, they remain expensive and require a designated environment. This limits their use in the routine assessment of movement, real-world testing of movement behavior, or studying movement behavior across long time periods. Improving accessibility to these measurements is crucial for the future of the industry. One solution to the above limitations would be to employ body-worn sensors. Sensors such as accelerometers [3], gyroscopes [4], and full inertial measurement units (IMUs) [5] have all been used to quantify movement. These systems have been shown to be reliable [6; 4; 7; 8] and valid [9; 10; 11; 12; 13] compared to an optoelectronic gold standard. Established validity and reliability of these sensor systems against optoelectronic gold standards have focused on the difference between measured peak angles [7; 9; 14]. This method of assessment lacks nuance and ecological validity compared to the anticipated use on humans, where data are likely to be evaluated over a range of movement, such as through movement behavior across time, and not just static peak values. In isolation, gyroscopes rely on integration from angular velocity to produce angular displacement, and this is prone to drift over time due to the integration of noise and non-zero gyroscope bias [15], making these of limited use for prolonged data collection sessions. IMUs, with their integrated technology and fusion algorithms, often use magnetometers to overcome challenges with sensor drift by providing an absolute heading reference [16]. However, the magnetic field vector is disturbed by metals and electronic devices, meaning that data collection in real-world environments is difficult [17]. Accelerometers, if used in isolation, provide estimates of linear acceleration but can be used to compute angles through the known relationship with the gravity vector [18]. The computation is relatively simple as it does not rely on complex fusion algorithms, and accelerometers do not have problems with drift or metallic interference, making them an attractive option for wearable sensors. However, the orientation of the accelerometer sensor is computed relative to the vertical gravity vector. Therefore, if the sensor is rotated about the vertical gravity vector, no change in orientation is determined, thus restricting the angles to only two planes of motion [19]. Furthermore, accuracy of the gravity vector is reliant on the sensors being relatively still, as any motion results in acceleration in addition to the acceleration of gravity. This results in the sensor output being a combination of accelerations, affecting further mathematical calculations. Whilst accelerometers have been applied to spinal movement analysis [10; 20], some important sources of error have not been systematically assessed, which could affect their accuracy for clinical use. Wearable sensors for human movement analysis are prone to many sources of error including, but not limited to, the sensor itself, human variability, and the interface between the sensor and human. This human-sensor interaction as a source of error is the error associated with the interface between the physical device (sensor) and the human, i.e., the attachment. Previous research has focused on errors associated with soft tissue artifacts, where there is movement of the soft tissues over the underlying bones [21; 22]. Whilst this is an important consideration, it exists for all surface methods of measuring motion. Other sources of human-sensor interaction error may include issues with the fixation such that the fixation fails non-uniformly, resulting in sensor movement which is not mapping body movement. However, error may also relate to the initial orientation of the sensor. Consistent alignment of the sensors axes to the human body is virtually impossible.