Expanding the Horizons of Wireless Sensing

All interactions of objects, humans, and machines with the physical world are via contact forces. For instance, objects placed on a table exert their gravitational forces, and the contact interactions via our hands/feet are guided by the sense of contact force felt by our skin. Thus, the ability to sense the contact forces can allow us to measure all these ubiquitous interactions, enabling a myriad of applications. Furthermore, force sensors are a critical requirement for safer surgeries, which require measuring complex contact forces experienced as a surgical instrument interacts with the surrounding tissues during the surgical procedure. However, with currently available discrete point-force sensors, which require a battery to sense the forces and communicate the readings wirelessly, these ubiquitous sensing and surgical sensing applications are not practical. This motivates the development of new force sensors that can sense, and communicate wirelessly without consuming significant power to enable a battery-free design. In this magazine article, we present WiForce, a low-power wireless force sensor utilizing a joint sensing-communication paradigm. That is, instead of having separate sensing and communication blocks, WiForce directly transduces the force measurements onto variations in wireless signals reflecting WiForce from the sensor. This novel trans-duction mechanism also allows WiForce to generalize easily to a length continuum, where we can detect as well as localize forces acting on the continuum. We fabricate and test our sensor prototype in different scenarios, including testing beneath a tissue phantom, and obtain sub-N sensing and sub-mm localizing accuracies (0.34 N and 0.6 mm, respectively).

Leveraging Smartphone Sensors for Detecting Abnormal Gait for Smart Wearable Mobile Technologies

Walking is one of the most common modes of terrestrial locomotion for humans. Walking is essential for humans to perform most kinds of daily activities. When a person walks, there is a pattern in it, and it is known as gait. Gait analysis is used in sports and healthcare. We can analyze this gait in different ways, like using video captured by the surveillance cameras or depth image cameras in the lab environment. It also can be recognized by wearable sensors. e.g., accelerometer, force sensors, gyroscope, flexible goniometer, magneto resistive sensors, electromagnetic tracking system, force sensors, and electromyography (EMG). Analysis through these sensors required a lab condition, or users must wear these sensors. For detecting abnormality in gait action of a human, we need to incorporate the sensors separately. We can know about one's health condition by abnormal human gait after detecting it. Understanding a regular gait vs. abnormal gait may give insights to the health condition of the subject using the smart wearable technologies. Therefore, in this paper, we proposed a way to analyze abnormal human gait through smartphone sensors. Though smart devices like smartphones and smartwatches are used by most of the person nowadays. So, we can track down their gait using sensors of these intelligent wearable devices. In this study, we used twenty-three (N=23) people to record their walking activities. Among them fourteen people have normal gait actions, and nine people were facing difficulties with their walking due to their illness. To do the stratification of the gait of the subjects, we have adopted five machine learning algorithms with addition a deep learning algorithm. The advantages of the traditional classification are analyzed and compared among themselves. After rigorous performance analysis we found support vector machine (SVM) showing 96% accuracy, highest among the tradition classifiers. 70%, 84%, and 95% accuracy is obtained by the logistic regression, Naïve Bayes, and k-Nearest Neighbor (kNN) classifiers, respectively. As per the state-of-the art, deep learning classifiers has been proven to outperform the traditional classifiers in similar binary classification problems. We have considered the scenario and applied the 2D convolutional neural network (2D-CNN) classification algorithm, which outperformed the other algorithms showing accuracy of 98%. The model can be optimized and can be integrated with the other sensors to be utilized in the mobile wearable devices.

Direct ink write multi-material printing of PDMS-BTO composites with MWCNT electrodes for flexible force sensors

With recent advances of additive manufacturing (AM) technology, direct ink write (DIW) printing has allowed to incorporate multi-material printing of various materials with freedom of design and complex geometric shapes to complete functional sensors in a one-step fabrication. This paper introduces the use of DIW 3D printing of polydimethylsiloxane (PDMS) with barium titanate (BTO) filler as stretchable composites with tunable piezoelectric properties that can be used for force sensors applications. To improve the bonding between stretchable piezoelectric composites and electrodes, multi-walled carbon nanotubes (MWCNT) was included in the fabrication of electrodes at a fixed ratio of 11 wt. %. The alignment of the BTO dipoles was achieved through corona poling method, which applies an electric charge on the surface layer of the functional material, aligning the dipoles in the desired direction and thus gaining the piezoelectricity. Different BTO mixing ratios (10-50 wt. %) were evaluated in order to obtain tunable piezoelectric properties and compare the sensitivity with respect their elastic properties. Tensile testing and piezoelectric testing were carried out to characterize mechanical and piezoelectric properties. Results showed that fabricated PDMS with 50 wt. % BTO gave the highest piezoelectric coefficient (d33) of 11.5 pC/N and with an output voltage of 385 mV under compression loading of >200 lbF. This demonstrates feasibility of using multi-material DIW printing to fabricate piezoelectric force sensors with integrated electrodes in one-step without compromising the flexibility of the material.