Assessment System for Predicting Maximal Safe Range for Heel Height by Using Force-Sensing Resistor Sensors and Regression Models.

Women often wear high-heeled shoes for professional or esthetic reasons. However, high-heeled shoes can cause discomfort and injury and can change the body's center of gravity when maintaining balance. This study developed an assessment system for predicting the maximal safe range for heel height by recording the plantar pressure of participants' feet by using force-sensing resistor (FSR) sensors and conducting analyses using regression models. Specifically, 100 young healthy women stood on an adjustable platform while physicians estimated the maximal safe height of high-heeled shoes. The collected FSR data combined with and without personal features were analyzed using regression models. The experimental results showed that the regression model based on the pressure data for the right foot had better predictive power than that based on data for the left foot, regardless of the module. The model with two heights had higher predictive power than that with a single height. Furthermore, adding personal features under the condition of two heights afforded the best predictive effect. These results can help wearers choose maximal safe high-heeled shoes to reduce injuries to the bones and lower limbs.

A Power-Efficient Sensing Approach for Pulse Wave Palpation-Based Heart Rate Measurement.

Heart rate (HR) is an essential indicator of health in the human body. It measures the number of times per minute that the heart contracts or beats. An irregular heartbeat can signify a severe health condition, so monitoring heart rate periodically can help prevent heart complications. This paper presents a novel wearable sensing approach for remote HR measurement by a compact resistance-to-microcontroller interface circuit. A heartbeat's signal can be detected by a Force Sensing Resistor (FSR) attached to the body near large arteries (such as the carotid or radial), which expand their area each time the heart expels blood to the body. Depending on how the sensor interfaces with the subject, the FSR changes its electrical resistance every time a pulse is detected. By placing the FSR in a direct interface circuit, those resistance variations can be measured directly by a microcontroller without using either analog processing stages or an analog-to-digital converter. In this kind of interface, the self-heating of the sensor is avoided, since the FSR does not require any voltage or bias current. The proposed system has a sampling rate of 50 Sa/s, and an effective resolution of 10 bits (200 mΩ), enough for obtaining well-shaped cardiac signals and heart rate estimations in real time by the microcontroller. With this approach, the implementation of wearable systems in health monitoring applications is more feasible.

Transfer of Learning from Vision to Touch: A Hybrid Deep Convolutional Neural Network for Visuo-Tactile 3D Object Recognition.

Transfer of learning or leveraging a pre-trained network and fine-tuning it to perform new tasks has been successfully applied in a variety of machine intelligence fields, including computer vision, natural language processing and audio/speech recognition. Drawing inspiration from neuroscience research that suggests that both visual and tactile stimuli rouse similar neural networks in the human brain, in this work, we explore the idea of transferring learning from vision to touch in the context of 3D object recognition. In particular, deep convolutional neural networks (CNN) pre-trained on visual images are adapted and evaluated for the classification of tactile data sets. To do so, we ran experiments with five different pre-trained CNN architectures and on five different datasets acquired with different technologies of tactile sensors including BathTip, Gelsight, force-sensing resistor (FSR) array, a high-resolution virtual FSR sensor, and tactile sensors on the Barrett robotic hand. The results obtained confirm the transferability of learning from vision to touch to interpret 3D models. Due to its higher resolution, tactile data from optical tactile sensors was demonstrated to achieve higher classification rates based on visual features compared to other technologies relying on pressure measurements. Further analysis of the weight updates in the convolutional layer is performed to measure the similarity between visual and tactile features for each technology of tactile sensing. Comparing the weight updates in different convolutional layers suggests that by updating a few convolutional layers of a pre-trained CNN on visual data, it can be efficiently used to classify tactile data. Accordingly, we propose a hybrid architecture performing both visual and tactile 3D object recognition with a MobileNetV2 backbone. MobileNetV2 is chosen due to its smaller size and thus its capability to be implemented on mobile devices, such that the network can classify both visual and tactile data. An accuracy of 100% for visual and 77.63% for tactile data are achieved by the proposed architecture.

Seat Occupancy Detection Based on a Low-Power Microcontroller and a Single FSR.

This paper proposes a microcontroller-based measurement system to detect and confirm the presence of a subject in a chair. The system relies on a single Force Sensing Resistor (FSR), which is arranged in the seat of the chair, that undergoes a sudden resistance change when a subject/object is seated/placed over the chair. In order to distinguish between a subject and an inanimate object, the system also monitors small-signal variations of the FSR resistance caused by respiration. These resistance variations are then directly measured by a low-cost general-purpose microcontroller unit (MCU) without using either an analogue processing stage or an analogue-to-digital converter. Two versions of such a MCU-based circuit are presented: one to prove the concept of the measurement, and another with a smart wake-up (generated by the sudden resistance change) intended to reduce the energy consumption. The feasibility of the proposed measurement system is experimentally demonstrated with subjects of different weight sitting at different postures, and also with objects of different weight. The MCU-based circuit with a smart wake-up shows a standby current consumption of 800 nA, and requires an energy of 125 µJ to carry out the measurement after the wake-up.

Sleeping posture recognition using fuzzy c-means algorithm.

BACKGROUND: Pressure sensors have been used for sleeping posture detection, which meet privacy requirements. Most of the existing techniques for sleeping posture recognition used force-sensitive resistor (FSR) sensors. However, lower limbs cannot be recognized accurately unless thousands of sensors are deployed on the bedsheet. METHOD: We designed a sleeping posture recognition scheme in which FSR sensors were deployed on the upper part of the bedsheet to record the pressure distribution of the upper body. In addition, an infrared array sensor was deployed to collect data for the lower body. Posture recognition was performed using a fuzzy c-means clustering algorithm. Six types of sleeping body posture were recognized from the combination of the upper and lower body postures. RESULTS: The experimental results showed that the proposed method achieved an accuracy of above 88%. Moreover, the proposed scheme is cost-efficient and easy to deploy. CONCLUSIONS: The proposed sleeping posture recognition system can be used for pressure ulcer prevention and sleep quality assessment. Compared to wearable sensors and cameras, FSR sensors and infrared array sensors are unobstructed and meet privacy requirements. Moreover, the proposed method provides a cost-effective solution for the recognition of sleeping posture.

Design of an Artificial Neural Network Algorithm for a Low-Cost Insole Sensor to Estimate the Ground Reaction Force (GRF) and Calibrate the Center of Pressure (CoP).

As an alternative to high-cost shoe insole pressure sensors that measure the insole pressure distribution and calculate the center of pressure (CoP), researchers developed a foot sensor with FSR sensors on the bottom of the insole. However, the calculations for the center of pressure and ground reaction force (GRF) were not sufficiently accurate because of the fundamental limitations, fixed coordinates and narrow sensing areas, which cannot cover the whole insole. To address these issues, in this paper, we describe an algorithm of virtual forces and corresponding coordinates with an artificial neural network (ANN) for low-cost flexible insole pressure measurement sensors. The proposed algorithm estimates the magnitude of the GRF and the location of the foot plantar CoP. To compose the algorithm, we divided the insole area into six areas and created six virtual forces and the corresponding coordinates. We used the ANN algorithm with the input of magnitudes of FSR sensors, 1st and 2nd derivatives of them to estimate the virtual forces and coordinates. Eight healthy males were selected for data acquisition. They performed an experiment composed of the following motions: standing with weight shifting, walking with 1 km/h and 2 km/h, squatting and getting up from a sitting position to a standing position. The ANN for estimating virtual forces and corresponding coordinates was fitted according to those data, converted to c script, and downloaded to a microcontroller for validation experiments in real time. The results showed an average RMSE the whole experiment of 31.154 N for GRF estimation and 8.07 mm for CoP calibration. The correlation coefficients of the algorithm were 0.94 for GRF, 0.92 and 0.76 for the X and Y coordinate respectively.

A new magnetic internal distractor: cadaveric study of changes in trapeziometacarpal joint forces.

Distraction is a new treatment for trapeziometacarpal joint osteoarthritis. The purpose of this study was to test the efficiency of magnetic distraction using a new internal distractor in cadavers. The distractor consists of two magnets embedded inside titanium capsules that are implanted on either side of the trapeziometacarpal joint with the same poles facing each other, so that the force between the magnets distracts the joint. Intra-articular forces were recorded pre-implantation, immediately after implantation and again 10 minutes later. We also studied the changes in the forces before and after the procedure in different thumb positions. Our findings show that the trapeziometacarpal joint could be offloaded in all the studied trapeziometacarpal positions.

Feasibility study of a force-sensing resistor device for landmark detection for neuraxial procedures and anesthesiologists' impact on device improvement.

The standard of care for neuraxial procedures is landmark palpation to determine the site of placement. This research study aimed to test the primary feasibility of VerTouch™, a force-sensing resistor device, to identify landmarks for the initiation of neuraxial procedures. Patients were recruited at the time of labour epidural, or when consenting for spinal anaesthesia for caesarean delivery at a single centre. The clinical team used the device to create a pressure map image of the bony spine. If they felt confident, they used the device guide to make a mark on the patient's back. If the mark was used, total insertions and redirections, combined as passes, of the needle during the procedure were counted for secondary outcome analysis. A total of 101 parturients were recruited, and the provider felt confident making a mark based on the imaging in 96.9% of cases. Device success (completion using the mark and ≤4 total passes) occurred in 91.4% of cases. This feasibility study showed that the primary outcome, the provider using the device to make a mark, was successful. In addition, based on comparing passes with historical data, the Vertouch™ device showed promise for future use to minimise needle manipulation in neuraxial procedures.

LabVIEW based monitoring and rehabilitation module for freezing of gait in Parkinson's disease.

Freezing of gait (FOG) is an episodic motor symptom that occurs in almost half of the patients having Parkinson's Disease (PD). Levodopa and other dopaminergic drugs, although quite effective for tremors and bradykinesia (dyskinesia) are not so effective in case of postural instability and freezing of gait. This establishes a need for a non-pharmacological intervention. It has been well established in research that PD patients who experience FOG respond positively to sensory cueing. In this paper, a complete modular system has been developed that has both: (i) biofeedback in the form of vibrational cueing, synchronised with gait (hardware), as well as (ii) real-time monitoring, and graph generation for offline analysis (software). To realise this, a force sensor fitted shoe is constructed to analyse gait and provide vibrations in sync with phases of gait, for rehabilitation therapy. The assembly is monitored on LabVIEW, where (i) a 5-level force heat map and a 3D pressure map qualitatively shows the force experienced by force sensing resistors (FSR), (ii) an indicator for each vibrational motor shows its on/off state, and (iii) a live voltage vs. time graph (separate for each foot) is generated.

Wearable step counting using a force myography-based ankle strap.

INTRODUCTION: Step counting can be used to estimate the activity level of people in daily life; however, commercially available accelerometer-based step counters have shown inaccuracies in detection of low-speed walking steps (<2.2 km/h), and thus are not suitable for older adults who usually walk at low speeds. This proof-of-concept study explores the feasibility of using force myography recorded at the ankle to detect low-speed steps. METHODS: Eight young healthy participants walked on a treadmill at three speeds (1, 1.5, and 2.0 km/h) while their force myography signals were recorded at the ankle using a customized strap embedded with an array of eight force-sensing resistors. A K-nearest neighbour model was trained and tested with the recorded data. Additional three mainstream machine learning algorithms were also employed to evaluate the performance of force myography band as a pedometer. RESULTS: Results showed a low error rate of the step detection (<1.5%) at all three walking speeds. CONCLUSIONS: This study demonstrates not only the feasibility of the proposed approach but also the potential of the investigated technology to reliably monitor low-speed step counting.

Motorized adaptive compression system for enhancing venous return: A feasibility study on healthy individuals.

Notwithstanding the extensive use of conventional compression devices in managing venous disorders, these modalities have shortages that diminish their treatment efficacy and lessen patient adherence to therapy. The purpose of this study was to develop an improved compression system that eliminates the flaws of the existing devices. A motorized bandage was designed that takes advantage of continuous feedback from force-sensing resistors to apply reproducible, controlled pressure on the lower extremities. The performance of the device in enhancing venous return was explored in a pilot test on 11 healthy participants, wherein graded lower body negative pressure was employed as a surrogate of passive standing. Each subject underwent two experiments; with and without pressure application over the calves. A two-way repeated-measures analysis of variance revealed a significant difference in the mean hemodynamic responses when the compression bandage was in action (p < .05). Specifically, a meaningful increase was observed in mean arterial pressure by 5%, diastolic blood pressure by 8% and left ventricular ejection time by 4%; and a significant decrease of 5% and 6% was noticed in heart rate and pulse pressure, respectively. These results demonstrate the capability of the designed system in attenuating the imposed orthostatic stress on cardiovascular system.

Body posture recognition and turning recording system for the care of bed bound patients.

This paper proposes body posture recognition and turning recording system for assisting the care of bed bound patients in nursing homes. The system continuously detects the patient's body posture and records the length of time for each body posture. If the patient remains in the same body posture long enough to develop pressure ulcers, the system notifies caregivers to change the patient's body posture. The objective of recording is to provide the log of body turning for querying of patients' family members. In order to accurately detect patient's body posture, we developed a novel pressure sensing pad which contains force sensing resistor sensors. Based on the proposed pressure sensing pad, we developed a bed posture recognition module which includes a bed posture recognition algorithm. The algorithm is based on fuzzy theory. The body posture recognition algorithm can detect the patient's bed posture whether it is right lateral decubitus, left lateral decubitus, or supine. The detected information of patient's body posture can be then transmitted to the server of healthcare center by the communication module to perform the functions of recording and notification. Experimental results showed that the average posture recognition accuracy for our proposed module is 92%.

Design of a Functional Training Prototype for Neonatal Resuscitation.

Birth Asphyxia is considered to be one of the leading causes of neonatal mortality around the world. Asphyxiated neonates require skilled resuscitation to survive the neonatal period. The project aims to train health professionals in a basic newborn care using a prototype with an ultimate objective to have one person at every delivery trained in neonatal resuscitation. This prototype will be a user-friendly device with which one can get trained in performing neonatal resuscitation in resource-limited settings. The prototype consists of a Force Sensing Resistor (FSR) that measures the pressure applied and is interfaced with Arduino(®) which controls the Liquid Crystal Display (LCD) and Light Emitting Diode (LED) indication for pressure and compression counts. With the increase in population and absence of proper medical care, the need for neonatal resuscitation program is not well addressed. The proposed work aims at offering a promising solution for training health care individuals on resuscitating newborn babies under low resource settings.