Knowledge Integration and Open Innovation in the Brazilian Cosmetics Industry

© Universidad Alberto Hurtado, Facultad de Economía y Negocios. This article is based on a thesis that examined open innovation in the Brazilian cosmetics sector and its relationship with knowledge integration, comparing less open and more open firms. The ability to integrate knowledge is related to competitive advantage, and this study sheds light onto OI at each different firm. The main findings show that, different levels of openness in innovation, demand firm-specific mechanism for KI. Also, openness increases complexity in management. The understanding of how firms select their knowledge for appropriation and differentiation is also considered. The Brazilian cosmetics market was chosen because it occupies the 3rd position in the world´s ranking and this industry is under researched. A cross-case comparison is used

a comparison of semg temporal and spatial information in the analysis of continuous movements

Abstract Much effort has recently been devoted to the analysis of continuous movements with the aim of promoting EMG signal acceptance in several fields of application. Moreover, several studies have been performed to optimize the temporal and spatial parameters in order to obtain a robust interpretation of EMG signals. Resulting from these perspectives, the investigation of the contribution of EMG temporal and spatial information has become a relevant aspect for signal interpretation. This paper aims to evaluate the effects of the two types of information on continuous motions analysis. In order to achieve this goal, the spatial and temporal information of EMG signals were separated and applied as input for an offline Template Making and Matching algorithm. Movement recognition was performed testing three different methods. In the first case (the Temporal approach) the RMS time series generated during movements was the only information employed. In the second case (the Spatial approach) the mean RMS amplitude measured on each channel was considered. Finally, in the third case (the Spatio-Temporal approach) a combination of the information from both the previous approaches was applied. The experimental protocol included 14 movements, which were different from each other in the muscular activation and the execution timing. Results show that the recognition of continuous movements cannot disregard the temporal information. Moreover, the temporal patterns seem to be relevant also for distinguishing movements which differ only in the muscular areas they activate

Quantifying Forearm Muscle Activity during Wrist and Finger Movements by Means of Multi-Channel Electromyography.

The study of hand and finger movement is an important topic with applications in prosthetics, rehabilitation, and ergonomics. Surface electromyography (sEMG) is the gold standard for the analysis of muscle activation. Previous studies investigated the optimal electrode number and positioning on the forearm to obtain information representative of muscle activation and robust to movements. However, the sEMG spatial distribution on the forearm during hand and finger movements and its changes due to different hand positions has never been quantified. The aim of this work is to quantify 1) the spatial localization of surface EMG activity of distinct forearm muscles during dynamic free movements of wrist and single fingers and 2) the effect of hand position on sEMG activity distribution. The subjects performed cyclic dynamic tasks involving the wrist and the fingers. The wrist tasks and the hand opening/closing task were performed with the hand in prone and neutral positions. A sensorized glove was used for kinematics recording. sEMG signals were acquired from the forearm muscles using a grid of 112 electrodes integrated into a stretchable textile sleeve. The areas of sEMG activity have been identified by a segmentation technique after a data dimensionality reduction step based on Non Negative Matrix Factorization applied to the EMG envelopes. The results show that 1) it is possible to identify distinct areas of sEMG activity on the forearm for different fingers; 2) hand position influences sEMG activity level and spatial distribution. This work gives new quantitative information about sEMG activity distribution on the forearm in healthy subjects and provides a basis for future works on the identification of optimal electrode configuration for sEMG based control of prostheses, exoskeletons, or orthoses. An example of use of this information for the optimization of the detection system for the estimation of joint kinematics from sEMG is reported

Clinical Features to Predict the Use of a sEMG Wearable Device (REMO®) for Hand Motor Training of Stroke Patients: A Cross-Sectional Cohort Study

After stroke, upper limb motor impairment is one of the most common consequences that compromises the level of the autonomy of patients. In a neurorehabilitation setting, the implementation of wearable sensors provides new possibilities for enhancing hand motor recovery. In our study, we tested an innovative wearable (REMO®) that detected the residual surface-electromyography of forearm muscles to control a rehabilitative PC interface. The aim of this study was to define the clinical features of stroke survivors able to perform ten, five, or no hand movements for rehabilitation training. 117 stroke patients were tested: 65% of patients were able to control ten movements, 19% of patients could control nine to one movement, and 16% could control no movements. Results indicated that mild upper limb motor impairment (Fugl-Meyer Upper Extremity 18 points) predicted the control of ten movements and no flexor carpi muscle spasticity predicted the control of five movements. Finally, severe impairment of upper limb motor function (Fugl-Meyer Upper Extremity > 10 points) combined with no pain and no restrictions of upper limb joints predicted the control of at least one movement. In conclusion, the residual motor function, pain and joints restriction, and spasticity at the upper limb are the most important clinical features to use for a wearable REMO® for hand rehabilitation training