Active-sensing systems such as echolocation provide animals with distinct advantages in dark environments. For social animals, however, like many bat species, active sensing can present problems as well: when many individuals emit bio-sonar calls simultaneously, detecting and recognizing the faint echoes generated by one's own calls amid the general cacophony of the group becomes challenging. This problem is often termed ‘jamming’ and bats have been hypothesized to solve it by shifting the spectral content of their calls to decrease the overlap with the jamming signals. We tested bats’ response in situations of extreme interference, mimicking a high density of bats. We played-back bat echolocation calls from multiple speakers, to jam flying Pipistrellus kuhlii bats, simulating a naturally occurring situation of many bats flying in proximity. We examined behavioural and echolocation parameters during search phase and target approach. Under severe interference, bats emitted calls of higher intensity and longer duration, and called more often. Slight spectral shifts were observed but they did not decrease the spectral overlap with jamming signals. We also found that pre-existing inter-individual spectral differences could allow self-call recognition. Results suggest that the bats’ response aimed to increase the signal-to-noise ratio and not to avoid spectral overlap.

The dynamics of the human fingertip enable haptic sensing and the ability to manipulate objects in the environment. Here we describe a wearable strain sensor, associated electronics, and software to detect and interpret the kinematics of deformation in human fingernails. Differential forces exerted by fingertip pulp, rugged connections to the musculoskeletal system and physical contact with the free edge of the nail plate itself cause fingernail deformation. We quantify nail warpage on the order of microns in the longitudinal and lateral axes with a set of strain gauges attached to the nail. The wearable device transmits raw deformation data to an off-finger device for interpretation. Simple motions, gestures, finger-writing, grip strength, and activation time, as well as more complex idioms consisting of multiple grips, are identified and quantified. We demonstrate the use of this technology as a human-computer interface, clinical feature generator, and means to characterize workplace tasks.

Assessment of body kinematics during performance of daily life activities at home plays a significant role in medical condition monitoring of elderly people and patients with neurological disorders. The affordable and non-wearable Microsoft Kinect (“Kinect”) system has been recently used to estimate human subject kinematic features. However, the Kinect suffers from a limited range and angular coverage, distortion in skeleton joints’ estimations, and erroneous multiplexing of different subjects’ estimations to one. This study addresses these limitations by incorporating a set of features that create a unique “Kinect Signature”. The Kinect Signature enables identification of different subjects in the scene, automatically assign the kinematics feature estimations only to the subject of interest, and provide information about the quality of the Kinect-based estimations. The methods were verified by a set of experiments, which utilize real-time scenarios commonly used to assess motor functions in elderly subjects and in subjects with neurological disorders. The experiment results indicate that the skeleton based Kinect Signature features can be used to identify different subjects in high accuracy. We demonstrate how these capabilities can be used to assign the Kinect estimations to the Subject of Interest, and exclude low quality tracking features. The results of this work can help in establishing reliable kinematic features, which can assist in future to obtain objective scores for medical analysis of patient condition at home while not restricted to perform daily life activities.

In recent years, the demand for high-precision tracking systems has significantly increased in the field of Wireless Sensor Network (WSN). A new tracking system based on exploitation of Received Signal Strength Indicator (RSSI) measurements in WSN is proposed. The proposed system is designed in particular for WSNs that are deployed in close proximity and can transmit data at a high transmission rate. The close proximity and an optimized transmit power level enable accurate conversion of RSSI measurements to range estimates. Having an adequate transmission rate enables spatial-temporal correlation between consecutive RSSI measurements. In addition, advanced statistical and signal processing methods are used to mitigate channel distortion and to compensate for packet loss. The system is evaluated in indoor conditions and achieves tracking resolution of a few centimeters which is compatible with theoretical bounds.

This paper suggests using a wideband sonar system to detect and classify human activity in indoor environment. The high bandwidth enables precise tracking of body parts, and its enhanced correlation properties can be used to distinguish between human and nonhuman objects. Maximal Likelihood (ML) criterions to derive kinematic features and processing methods to estimate the subject activity level and activity type were derived and tailored to the wideband sonar. For tracking and association of the echoes reflected from the different body part, an efficient approximation of the sequential ML estimator was derived in the natural time-space domain, which eases the exploitation of the a priori knowledge about the human subject target. For classification of the activity, a weighted two level nested k-nearest neighbor classifier was applied on only four kinematic features. A set of experiments with five subjects, performing three different activity types of standing, walking, and swinging upper limbs, was carried out in a typical indoor environment. The proposed technology has managed to classify well the different activity types and demonstrated the potential of this technology for continuous assessment of various kinematic features of humans in indoor environment with reduced costs, under any light, smoke, or humidity conditions. This can be useful for instance for monitoring patients at home, and for detecting intruders.