## 4 Discussion
While grip force may initially be stronger in the dominant hand, this can reverse spontaneously depending on such anthropometric limitations or the necessity of a given task and specific environmental constraints [15; 16; 19]. In healthy subjects [15], grip strength is fairly precisely scaled to just a little stronger than the minimum necessary to prevent an object from slipping out of the hand. Adaptive processes in finger force control during motor learning [13; 31; 32] explain why the contribution of each finger to overall grip strength, coarse adjustments, or finer grip force control may vary across tasks and their requirements for motor planning and execution. The index, middle, and ring fingers are deemed critical for lifting and/or manipulating weighted cylindrical objects, such as the handles in this study, in various directions [2; 33; 4; 5; 6; 17; 3]. The amount of force applied by each digit depends on anthropometric factors in interaction with object constraints, including where exactly the fingers or sensors are placed when picking up or manipulating an object [12; 31]. In the absence of such constraints, the complex anatomy of the human hand allows for a large number of postures in the generation of adapted and stable grip modes. These functional synergies, and the complex problem of motor redundancy, have been discussed in detail elsewhere [1; 12; 31; 32; 34]. Adaptive constraints and their interaction with anthropometric factors such as hand and finger dimensions explain why sensor position is such a critical factor, as shown in this study here. Sensor M3 on the middle phalanx of the ring finger produced the strongest grip strength signals in both hands, while sensor M1 on the middle phalanx of the index finger produced the weakest signals in the dominant hand. A considerable, statistically highly significant, difference in grip strength between dominant and non-dominant hands was recorded from sensor M2 on the middle phalanx of the middle finger, where forces produced by the dominant hand were statistically as strong as those in sensor M3, while the forces produced by the non-dominant hand were the weakest by comparison with either of the other two sensors. The sensor glove prototype used here has its own constraints, and, despite the fact that the material the sensors are sewn into stretches a bit, requirements for hand-fingerdimensions within a limited range need to be fulfilled for an optimal fit generating the desired optimal positioning of the sensors. The prototype here was developed in 2017 for a highly specific purpose. New biosensor technology may help overcome limitations of this model. During the last few years, polymers for artificial sensors that emulate the properties of natural sense organs, with improved selectivity for finely tuned rapid measurement, have begun to replace classical sensors and materials [35]. Combined with nanotechnology exploiting intrinsic or extrinsic functional advantages of polymers and polymer gels [36], novel generations of biosensor devices now open promising perspectives for robotics [36] and wearable biosensor technology in general [37]. The biomechanical and neurocognitive complexity of motor programs associated with spontaneous grip force anticipation and/or deployment are highly complex. Previous studies have shown that the contribution of any given finger or region of the palm to overall grip strength, coarse adjustments, anticipatory, or fine grip force control may vary across tasks, environments, and context-specific requirements for motor planning and execution, in interaction with anthropometric factors such as hand and finger dimensions. Recent findings suggest that grip control function may be directly linked to cognitive or mental ability [38], and that grip strength could be a reliable biomarker of brain health in general [39; 40].