Do Zebra Finch Parents Fail to Recognise Their Own Offspring?

Individual recognition systems require the sender to be individually distinctive and the receiver to be able to perceive differences between individuals and react accordingly. Many studies have demonstrated that acoustic signals of almost any species contain individualized information. However, fewer studies have tested experimentally if those signals are used for individual recognition by potential receivers. While laboratory studies using zebra finches have shown that fledglings recognize their parents by their “distance call”, mutual recognition using the same call type has not been demonstrated yet. In a laboratory study with zebra finches, we first quantified between-individual acoustic variation in distance calls of fledglings. In a second step, we tested recognition of fledgling calls by parents using playback experiments. With a discriminant function analysis, we show that individuals are highly distinctive and most measured parameters show very high potential to encode for individuality. The response pattern of zebra finch parents shows that they do react to calls of fledglings, however they do not distinguish between own and unfamiliar offspring, despite individual distinctiveness. This finding is interesting in light of the observation of a high percentage of misdirected feedings in our communal breeding aviaries. Our results demonstrate the importance of adopting a receiver's perspective and suggest that variation in fledgling contact calls might not be used in individual recognition of offspring.

Evaluation of Three State-of-the-Art Classifiers for Recognition of Activities of Daily Living from Smart Home Ambient Data

Smart homes for the aging population have recently started attracting the attention of the research community. The "health state" of smart homes is comprised of many different levels; starting with the physical health of citizens, it also includes longer-term health norms and outcomes, as well as the arena of positive behavior changes. One of the problems of interest is to monitor the activities of daily living (ADL) of the elderly, aiming at their protection and well-being. For this purpose, we installed passive infrared (PIR) sensors to detect motion in a specific area inside a smart apartment and used them to collect a set of ADL. In a novel approach, we describe a technology that allows the ground truth collected in one smart home to train activity recognition systems for other smart homes. We asked the users to label all instances of all ADL only once and subsequently applied data mining techniques to cluster in-home sensor firings. Each cluster would therefore represent the instances of the same activity. Once the clusters were associated to their corresponding activities, our system was able to recognize future activities. To improve the activity recognition accuracy, our system preprocessed raw sensor data by identifying overlapping activities. To evaluate the recognition performance from a 200-day dataset, we implemented three different active learning classification algorithms and compared their performance: naive Bayesian (NB), support vector machine (SVM) and random forest (RF). Based on our results, the RF classifier recognized activities with an average specificity of 96.53%, a sensitivity of 68.49%, a precision of 74.41% and an F-measure of 71.33%, outperforming both the NB and SVM classifiers. Further clustering markedly improved the results of the RF classifier. An activity recognition system based on PIR sensors in conjunction with a clustering classification approach was able to detect ADL from datasets collected from different homes. Thus, our PIR-based smart home technology could improve care and provide valuable information to better understand the functioning of our societies, as well as to inform both individual and collective action in a smart city scenario.