## 1 Introduction  
Motion is vital for human activities, contributing to the overall quality of life [1, 2, 3, 4, 5]. The ability to engage in physical activity is essential for good health and longevity, reducing the onset of a wide range of diseases including cardiovascular disease, hypertension, diabetes, and obesity, while enhancing overall resilience [1, 6, 7, 8, 9, 10]. Motion and mobility, however, are often altered with increasing age, injury, and disease, contributing to morbidity and mortality. In older adults the ability to move functionally, e.g., completing activities of daily living, is a vital means of preventing decline, disability, and mortality, while preserving independence [1, 2, 11, 12]. To foster personal, rehabilitative, and therapeutic engagement in motion activities, systems, and methods readily accessible to quantitate human motion will further enhance this goal. These tools will provide descriptors and metrics of motion, affording a means for evaluation and serial comparison, and personalization of training and therapeutic regimes [13, 14, 15, 16]. A range of approaches presently exists for assessing and quantitating human motion. These may be broadly categorized as \"around body\" or \"on body\" systems. Around body systems include three-dimensional motion lab configurations utilizing wall mounted or similarly affixed cameras, with markers placed on the subject [15, 16, 17, 18, 19]. These systems track the subject and calculate motion and motion components via video image capture, digitization, and signal analysis [13, 20]. 3-D motion capture technology has proven to be highly accurate in assessing biomechanical parameters of motion useful in the diagnosis of a variety of musculoskeletal and neurological conditions [15, 16, 19, 21]. To further simplify this approach, advances have been made through the development of 2-D systems less reliant on complex fixed hardware [18, 19, 21, 22, 23]. Previous studies using 2-D images have developed 3-D skeleton models and renderings revealing body motion information, e.g., during walking employing a single KinectTM camera or while performing simple body poses using a single depth camera [24, 25]. While real-time 3-D skeleton estimation from 2-D images is robust, additional validation and definition of their utility limits is needed before they can gain greater clinical acceptance [26]. Overall, while simpler to use, 2-D systems may have increased error and decreased accuracy under certain use conditions, making them less reliable for implementation in the healthcare setting [13, 20, 23]. Although there have been advances in computer technology related to improving the accuracy of 2-D systems in both marker-based and marker-less systems, 3-D marker-based systems are still considered the gold standard for the measurement of human biomechanical movement [27, 28, 29, 30]. On body systems, in contrast, include a range of devices that are placed directly on the individual, i.e., on the torso, limb, or digit, with contained sensors. These include skin pendants, bracelets, and skin patch systems typically with contained accelerometers, gyroscopes, and related sensors [15, 16, 31, 32, 33, 34, 35]. These systems, while highly accurate, have the limitation of often requiring multiple sensors to be placed on a given body part to be measured, and require multiple devices to construct an overall motion description. Inertial measurement units (M/IMU) are another form of on-body systems that are being advanced that use a combination of accelerometers, gyroscopes, and/or magnetometers to obtain and quantify kinematic data [36, 37]. However, while valid devices, more research is needed to advance the accuracy and utility of these systems for human kinematic assessment [36, 37]. As such, a need exists for a simple system of motion capture and quantitation free from constraints of being formally fixed in place or needing multiple on-body devices that is readily accessible, with minimal hardware dependence, yet with great accuracy.