PurposeThe paper aims to address the modelling and optimization of fully flexible assembly systems (F‐FAS), a new concept in flexible automation recently introduced by the authors.Design/methodology/approachThe paper presents a mathematical model of the F‐FAS, which makes it possible to predict its efficiency, throughput and unit direct production costs, correlating such values with system and production variables. The mathematical model proposed in the paper was derived from experimental and simulation data, which were analysed for a wide range of different productions and system settings.FindingsCorrelation analysis revealed that there are three main determinants of the efficiency of the F‐FAS: the number of components (types of parts) used to assemble the models (production variable); the average complexity of the models to be assembled (production variable); the ratio of the average perimeter of components (production variable) over a significant dimension of the working plane (system variable). Such parameters makes it possible to estimate the maximum attainable efficiency of the F‐FAS, and to calculate the optimal setting of the feeder which makes it possible to obtain such efficiency during the execution of the whole production order.Originality/valueThe model presented in the paper makes it possible to quantify in advance the real potential of the F‐FAS, according to the characteristics of the production mix and type of components to be assembled. By using the methodologies presented in the paper, one can first evaluate the convenience of the F‐FAS approach with respect to traditional FAS technology and manual assembly, then identify the optimal design and settings of the F‐FAS, according to the needs of a specific application. As a result, not only can the investment on the automated assembly system be accurately evaluated in advance, but also the return on investment can be maximized.

Bipedal gait can be stabilized through mechanically-appropriate mediolateral foot placement, although this strategy is disrupted in a subset of neurologically injured individuals with balance deficits. The goal of the present work was to develop a device to influence mediolateral foot placement during treadmill walking. We created a novel force-field using a combination of passive elasticity and active control; wires in series with extension springs run parallel to the treadmill belts and can be rapidly repositioned to exert mediolateral forces on the legs of users. This mechanical structure creates a channel-like force landscape that resists displacements of each leg away from its prescribed mediolateral position, producing near-linear effective mediolateral stiffness. The depth of these force-field channels can be predictably controlled by manipulating extension spring initial tension. In human testing, we found that the force-field can effectively "get-out-of-the-way" when desired, closely following the mediolateral leg trajectory with a delay of approximately 110 ms. The force-field can also encourage users to adjust their mediolateral foot placement in order to walk with either narrower or wider steps, without interfering with forward gait progression. Future work will test whether this novel device can help retrain a stable gait pattern in clinical populations.

Background During upper extremity (UE) stroke rehabilitation it is critical to match patient-ability to task-difficulty to promote neural reorganization and UE skill re-learning. However, there are few methods to do so. A Fugl-Meyer Upper Extremity Assessment (FMA-UE) “keyform,” derived from Rasch Analysis informed and progressed an UE rehabilitation program. Objective Test the feasibility of the keyform method for systematically planning and progressing rehabilitation. We hypothesized that optimally-challenging task-practice will maximize UE motor skill reacquisition. Methods Inclusion criteria: ischemic stroke >3 mo., voluntarily shoulder flexion ≥30° and simultaneous elbow extension ≥20°. The keyform method defined initial targets (goals) and progressed therapy after every 3rd session. Therapy targets were practiced within the context of client-selected functional tasks not in isolation. Feasibility was defined by subject pain/fatigue, UE motor function (Wolf Motor Function Test, WMFT) and movement patterns (kinematics). Assessments were administered pre- and post-treatment and compared with paired t-tests. Task-difficulty and patient-ability measures were calculated with Rasch analysis and compared with paired t-tests (p<0.05). Results Ten subjects (59.70±9.96 yrs., 24.1±30.54 mo. post-stroke) participated in 9 sessions, 200 movement repetitions/session in <2 hrs without pain or fatigue. Subjects gained UE motor function (WMFT: Pre 22.23±24.26 seconds, Post 15.46±22.12 seconds, p=0.01), improved shoulder-elbow coordination (index of curvature: Pre 1.30±0.15, Post 1.21±0.11, p=0.01) and exhibited reduced trunk compensatory movement (trunk displacement: Pre 133.97±74.15 mm, Post 108.08±64.73 mm, p=0.02). Task-difficulty and patient-ability measures were not statistically different throughout the program (Person-ability measures of 1.01±0.05, 1.64±0.45 and 2.22±0.65 logits and item difficulty measures of 0.93±0.37, 1.70±0.20, and 2.06±0.24 logits at the 3 testing time points respectively, p>0.05). Conclusion The FMA-UE keyform is a feasible method to assure that the difficulty of tasks practiced were well matched to initial and evolving levels of UE motor ability.

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