Parkinson’s disease (PD) affects over six million people globally. PD leads to devastating chronic motor manifestations such as bradykinesia/akinesia, rigidity, gait disturbance, and tremor. PD symptoms are managed by adjusting the schedule and dose of PD medications such as levodopa and dopamine agonists. However, PD patients at mid- and advanced-stages of the disease frequently experience additional treatment-related motor complications such as troubling motor fluctuations between mobile (ON) and akinetic (OFF) states and abnormal, involuntary dyskinetic movements [1]. At this stage, effective medication adjustments require accurate knowledge about the nature of patients’ motor complications over a typical day. The current clinical protocol entails obtaining this information through periodic clinical examinations and patient interviews. However, patient interviews can be unreliable and limited by recall bias [2]. Clinical examinations may only provide a snapshot of motor functioning, hence failing to capture an accurate picture of motor complications [3]. Rapid advancements in sensing technologies provide user-friendly wearables with a long battery life that can be worn by PD patients and used to unobtrusively assess motor symptoms during activities of daily living (ADL). Such sensing technologies can be tailored for use in monitoring PD patients at home to generate clinically actionable information that can be provided to the treating physician to make individualized therapeutic recommendations (Table 1) [4,5]. Commercially available wearable devices such as Kinesia360™ (Great Lakes NeuroTechnologies), Personal KinetiGraph® (PKG®) (Global Kinetics Corporation Ltd.), REMPARK (Sense4Care), and PERFORM [6] have various characteristics and deliverables to assess motor complications, but adoption and implementation in clinical practice have been slow and limited [7]. Contributing factors to such inconsistency might include delays in patient acceptance and adherence to wearing new technology, clinician acceptance, or issues with cost and insurance coverage. However, in an attempt to look beyond the technology life cycle to better understand this inconsistency, we sought to review the current status of these devices and explore possible gaps between the capabilities of their underlying algorithms compared to the requirements for an accurate motor complication monitoring system that can facilitate effective therapeutic adjustments. It is our view that a major factor contributing to slow adoption of at-home monitoring systems of motor complications is based on the practical utility of the existing deliverables from such devices, which are mainly determined by their underlying algorithms. We propose that if revised algorithms can be used to generate data that can be interpreted more reliably in the context of widely accepted and understood clinical measures, this would create more convincing evidence for the clinicians to utilize these devices and the insurance companies to support coverage. Table 1 Clinically actionable information required for precise identification of therapeutic goals and example medication adjustments to optimize PD control.