Efficient routing protocol development requires a proper network topology as it effects the overall performance of the communication system [19]. Proper network topology is very important for WBSNs because of the energy constraint, body postural movements, heterogeneous nature of the sensors and short transmission range. Some researchers use single hop communication, where each node communicates directly with the destination, while others use cluster based multi-hop routing, and are discussed later in this paper.3.2. Topological PartitioningThe network topology of WBSNs often faces the problem of disconnection or partitioning because of body postural movements and short range transmissions. Different researchers have tried to solve the problem of disconnection and partitioning in different ways.

For example, the authors of [20] use Line-of-Sight (LoS) and None-Line-of-Sight (NLoS) communication, while the authors of [21�C23] use store-and-forward routing to solve this problem. Therefore, the proposed routing protocols should take care of the different topological changes.3.3. Energy EfficiencyEnergy efficiency covers both the local energy consumption of nodes and the overall network lifetime. For implanted bio-medical sensors, it is not possible to replace the power source, while for wearable bio-medical sensors replacing the batteries might lead to discomfort of patients. Therefore, both energy consumption and network lifetime are major challenges in wireless body sensor networks. Communication among the sensor nodes consumes more energy as compared to sensing and processing [24].

Any proposed algorithm should be able to use different paths and/or nodes to send the data instead of depending on a single path and/or node preventing the consumption of total energy of that specific node(s). In [22], the authors define the network life as the time from which the network starts till the time Dacomitinib when the first node of the network expires. The network life is very much important in WBSNs because of energy constraints and the impossibility of replacing the energy source for implanted sensors.3.4. Limited ResourcesAlong with limited energy source, WBSNs also have short Radio Frequency (RF) transmission range, poor computation capabilities, limited storage capacity, as well as low bandwidth��which may keep on changing due to noise and other interferences [19]. Researchers must be aware of the limited resources when designing routing protocols for WBSNs.3.5. Quality of Service (QoS)In WBSNs different types of data require different quality of services as it deals with vital signs of the human body. The authors in [25,26] have classified the patient data into critical data (like EEG, ECG etc.

Impedance force control is very practical in the field of robotic compliance control and the main concept is based on the impedance equation which is the relationship between force and position/velocity error [1].Many researchers have improved the performance of the impedance control and expanded the application range since it was primarily proposed by Hogan [2�C5]. However, the classical impedance control is unsatisfying when the environment parameters are not exactly known. To overcome this problem, Lasky et al. [6] proposed a two-loop control system that the inner-loop is a classical impedance controller and the outer-loop is a trajectory modified for force-tracking. This algorithm uses the outer-loop to automatically modify the reference position by a simple force-feedback scheme when the environment is not exactly known.

Jung et al. [1] proposed an adaptive impedance control. The main idea of this algorithm is to minimize the force error directly by using a simple adaptive gain when the environment is changed. Seraji [7] proposed an adaptive admittance control based on the concept of mechanical admittance, which relates the contact force to the resulting velocity perturbation. Two adaptive PID and PI force compensators are designed in Seraji’s paper.In this paper an adaptive impedance control is proposed that uses an adaptive PID force compensator as an offset to adjust the output of the impedance controller when the environment position or stiffness is changed. It is a way that adjusts the impedance parameters indirectly, which is different from Jung’s.

In order to validate the algorithm, a joint simulation with MATLAB and ADAMS is presented. Firstly, the model of the tendon-driven dexterous hand is built in ADAMS referring to Cilengitide the robot hand of Robonaut-2, which is the first humanoid robot in space and has the typical tendon-driven dexterous hands [8]. A three-DOF finger of the robot hand is chosen as the research object. Then a control module of the robot finger is generated in ADAMS. Finally, the control system is built in MATLAB using the control module. The results of the joint simulation demonstrate that the proposed algorithm is robust. In addition, the position controller and inverse kinematics solver are designed for the tendon-driven finger.2.

?Features of the Robot Hand and Dynamic ModelThe model of the tendon-driven dexterous hand in ADAMS consists of four three-DOF fingers, a four-DOF thumb and a palm, as shown in Figure 1. For the three-DOF fingers, the fingertip’s motion depends on the coupled link, as shown in Figure 1. The actuation system of the robot hand is remotely packaged in the forearm, which makes the size of the robot hand as large as a man’s hand. Each unit of the actuation system consists of a brushless motor and a lead screw. The lead screw can convert rotary motion to linear motion. Each of the tendons connects the finger joint and the lead screw.

The use of a polar co-monomer (HEMA), which provides the stability of the nanospheres in water, and a hydrophobic polymer (PA), allows to produce a co-polymer with charged surface and hydroxyl groups on the particles surface. These properties of the nanospheres improve their adhesion on substrates, the high order of wide domains of CIM coating and the capability to bind polar molecules. The resulting chemical sensor has been tested at different relative humidity values.Figure 5.Chemical structure of P(PA/HEMA) (a). Morphology of the nanostructured polymer film used as CIM as seen at SEM (b).To implement the sensing unit, different masses of an aqueous solution of the nano-structured polymer P(PA/HEMA) have been deposited on the four channels of the MQCM and on four single QCM by casting technique.

Once the solvent has evaporated, a thin film of the material has remained on the substrate. According to Sau
Each biosensor has two primary components: bio-recognition element and transducer. The bio-recognition element, such as antibody and phage, is highly specific to the target species [1-4]. The reaction between the target species and the bio-recognition unit would result in some changes in the physical/chemical properties of the recognition unit. These changes are measured using a transducer. Different types of transducers have been developed and extensively investigated in recent years. One important type of the transducer is the acoustic wave (AW) device [5-14], which is an acoustic resonator and works as a mass sensor.

That is, the reaction between the bio-recognition component and the target species results in a change AV-951 in the mass load of the transducer/resonator, which shifts the resonance frequency. Thus, by monitoring the resonance frequency of the AW device, the reaction between the bio-recognition unit and the target species, such as captured bacterium cells by antibody/phage, can be determined. An AW device as a transducer used in biosensors is characterized using two critical parameters: mass sensitivity (Sm) and quality merit factor (or Q value) [9, 12, 14-16]. The mass sensitivity is defined as the shift in resonance frequency due to the attachment of a unit mass, while the Q value reflects the mechanical loss of the devices and characterizes the sharpness of the resonance peak in the amplitude/phase versus frequency plot.

A higher Sm means a more sensitive device, while a higher Q value represents a capability to determine a smaller change in resonance frequency (i.e. a higher resolution in determining resonance frequency). Therefore, it is highly desirable for an AW device to have a higher Sm and a larger Q value. Among all AW devices, micro/nano-cantilever exhibits extremely high sensitivity primarily due to its small mass [17-20]. For example, the detection of a mass as small as 10-18 g using cantilever has been demonstrated.

In these activities, feeding means the ability to feed oneself food after it has been prepared and made available. Therefore, eating and drinking detection is a very important topic for daily life surveillance. Measurement of eating or drinking activities in daily life or continuous recording of these activities at home would provide more reliable diagnosis of disabilities for hospitals or insurance companies. However, eating and drinking detection poses a challenge for the state of the art of the research in activity recognition [4], and few references or systematic methods can be found in the literature.In the daily life surveillance system, if the human activities (such as eating or drinking) can be tracked accurately, the results can help greatly and readily improve the ability of the identification of the whole system.

Therefore, devices that can accurately track the pose of limbs in space are essential components of such a surveillance system.One method of tracking and monitoring activities is via tracking the pose of human limbs in space. The human limb tracking system can be classified as non-vision based and vision-based systems. Non-vision based systems use inertial, mechanical and magnetic sensors etc. to continuously collect movement signals. For example, the Micro-ElectroMechanical Systems (MEMS) inertial and magnetic sensor devices [5, 6, 7, 8] can be used in most circumstances without limitations (i.e. illumination, temperature, or space, etc.) and show better performance in accuracy against mechanical sensors.

The main drawback of using inertial sensors is that accumulating errors (or drift) can become significant after a short period of time. Vision-based systems are widely used in recent Cilengitide years, such as [9, 10, 11, 12]. However, most vision-based approaches to human movement tracking involve intensive computations, such as temporal differencing, background subtraction or occlusion handling. In many cases, once a prior knowledge of an estimation of object kinematics is available, the expensive image detector array appears inefficient and unnecessary.Accelerometry-based activity analysis has been developed fast in recent years. Some prototype systems which aim at monitoring daily activities [13], conducting gait analysis [14], etc. are reported. In our system, the 3D accelerometers are applied to collect raw measurement data of the moving arm and the server computer communicates with the sensor devices via the blue-tooth. The simple hardware structure makes the data acquisition and processing easy.