Recently, 3D porous graphene-polymer composite-based piezoresistive sensors have gained significant traction in the field of flexible electronics owing to their ultralightweight nature, high compressability, robustness, and excellent electromechanical properties. In this work, we present an improved facile recipe for developing repeatable, reliable, and linear 3D graphene-polydimethylsiloxane (PDMS) spongy sensors for internet of things (IoT)-enabled wearable systems and smart consumer products. Fundamental morphological characterization and sensing performance assessment of the piezoresistive 3D graphene-polymer sensor were conducted to establish its suitability for the development of squeezable, flexible, and skin-mountable human motion sensors. The density and porosity of the sponges were determined to be 250 mg cm-3 and 74% respectively. Mechanical compressive loading tests conducted on the sensors revealed an average elastic modulus as low as ∼56.7 kPa. Dynamic compressive force-resistance change response tests conducted on four identical sensors revealed a linear piezoresistive response (in the compressive load range 0.42-2.18 N) with an average force sensitivity of 0.3470 ± 0.0794 N-1. In addition, an accelerated lifetime test comprising 1500 compressive loading cycles (at 3.90 N uniaxial compressive loading) was conducted to demonstrate the long-term reliability and stability of the sensor. To test the applicability of the sensors in smart wearables, four identical graphene-PDMS sponges were configured on the fingertip regions of a soft nitrile glove to develop a pressure sensing smart glove for real-time haptic pressure monitoring. Similarly, the sensors were also integrated into the Philips 9000 series electric shaver to realize smart shaving applications with the ability to monitor shaving motions. Furthermore, the readiness of our system for next-generation IoT-enabled applications was demonstrated by integrating the smart glove with an embedded system software utilizing the an open source microcontroller platform. The system was capable of identifying real-time qualitative pressure distribution across the fingertips while grasping daily life objects, thus establishing the suitability of such sensors for next-generation wearables for prosthetics, consumer devices, and personalized healthcare monitoring devices.