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Currently, several studies have been published using sensorized insoles for estimating ground reaction force using plantar pressure. However, information on design parameters, manufacturing techniques and guidelines for developing insoles is scarce, often leaving gaps that do not allow reproducing the insole. This study aims to empirically investigate the main parameters of constructing a sensorized insole for application in human gait.

Two devices were built to evaluate the force sensors. The first focuses on the construction of the sensors with different settings: the density of the sensor’s conductive trails (thickness and distance of the trails) and the inertia of the sensors (use of spacers to prevent unwanted readings). The second device focuses on the data capture and processing system: resolution of the analog–digital converter, acquisition rate and sensor activation level.

The resolution increase of the analog–digital converter and acquisition rate do not contribute to noise increase. Reducing the sensors’ coverage area can increase sensorized insole capacity. The inertia of the sensors can be adjusted using spacers without changing the electrical circuit and acquisition system.

Most sensorized insoles use commercial sensors. For this reason, it is not possible a full customization. This paper maps the main variables to manufacture custom sensors and data acquisition systems. This work also presents a case study where it is possible to see the influence of the parameters in the correlation between the sensorized insole and an instrumented treadmill with a force platform.

Fall detection is a primary necessity for elderly people with medically tested nervous problems. This paper aims important to detect fall and prevent fatal injuries and untreated attention for long hours.

The project is focused on developing a smart shoe with force-sensitive resistors placed at plantar pressure points to detect fall. This could draw immediate medical attention to the patient. The device is developed using sensors, microcontroller and accelerometer integrated into a compact module. A rule-based detection algorithm helps in transmitting the alert to an Internet of Things device when a fall is detected.

Based on the pressure applied, there is a change in resistive value of force sensitivity resistor. When it reaches the threshold value, fall gets detected and alert gets triggered through telegram bot with latitude and longitude details of the location.

The challenge in developing this device is to make it wearable reducing the overall hardware complexity. The entire module placed inside the sole of the shoe avoids inconvenience to the patients.

This paper is a conference report.

This paper presents the review of Consumer Electronics Show (CES) 2016.

impression mc>Originality/value new writing.

Plantar force is the interface pressure existing between the foot plantar surface and the shoe sole during static or dynamic gait. Plantar force derived from gait and posture plays a critical role for rehabilitation, footwear design, clinical diagnostics and sports activities, and so on. This paper aims to review plantar force measurement technologies based on piezoelectric materials, which can make the reader understand preliminary works systematically and provide convenience for researchers to further study.

The review introduces working principle of piezoelectric sensor, structures and hardware design of plantar force measurement systems based on piezoelectric materials. The structures of sensors in plantar force measurement systems can be divided into four kinds, including monolayered sensor, multilayered sensor, tri-axial sensor and other sensor. The previous studies about plantar force measurement system based on piezoelectric technology are reviewed in detail, and their characteristics and performances are compared.

A good deal of measurement technologies have been studied by researchers to detect and analyze the plantar force. Among these measurement technologies, taking advantage of easy fabrication and high sensitivity, piezoelectric sensor is an ideal candidate sensing element. However, the number and arrangement of the sensors will influence the characteristics and performances of plantar force measurement systems. Therefore, it is necessary to further study plantar force measurement system for better performances.

So far, many plantar force measurement systems have been proposed, and several reviews already introduced plantar force measurement systems in the aspect of types of pressure sensors, experimental setups for foot pressure measurement analysis and the technologies used in plantar shear stress measurements. However, this paper reviews plantar force measurement systems based on piezoelectric materials. The structures of piezoelectric sensors in the measurement systems are discussed. Hardware design applied to measurement system is summarized. Moreover, the main point of further study is presented in this paper.

The purpose of this paper is to present gait analysis for five different terrains: level ground, ramp ascent, ramp descent, stair ascent and stair descent.

Gait analysis has been carried out using a combination of the following sensors: force-sensitive resistor (FSR) sensors fabricated in foot insole to sense foot pressure, a gyroscopic sensor to detect the angular velocity of the shank and MyoWare electromyographic muscle sensors to detect muscle’s activities. All these sensors were integrated around the Arduino nano controller board for signal acquisition and conditioning purposes. In the present scheme, the muscle activities were obtained from the tibialis anterior and medial gastrocnemius muscles using electromyography (EMG) electrodes, and the acquired EMG signals were correlated with the simultaneously attained signals from the FSR and gyroscope sensors. The nRF24L01+ transceivers were used to transfer the acquired data wirelessly to the computer for further analysis. For the acquisition of sensor data, a Python-based graphical user interface has been designed to analyze and display the processed data. In the present paper, the authors got motivated to design and develop a reliable real-time gait phase detection technique that can be used later in designing a control scheme for the powered ankle-foot prosthesis.

The effectiveness of the gait phase detection was obtained in an open environment. Both off-line and real-time gait events and gait phase detections were accomplished for the FSR and gyroscopic sensors. Both sensors showed their usefulness for detecting the gait events in real-time, i.e. within 10 ms. The heuristic rules and a zero-crossing based-algorithm for the shank angular rate correctly identified all the gait events for the locomotion in all five terrains.

This study leads to an understanding of human gait analysis for different types of terrains. A real-time standalone system has been designed and realized, which may find application in the design and development of ankle-foot prosthesis having real-time control feature for the above five terrains.

The noise-free data from three sensors were collected in the same time frame from both legs using a wireless sensor network between two transmitters and a single receiver. Unlike the data collection using a treadmill in a laboratory environment, this setup is useful for gait analysis in an open environment for different terrains.

In retail, product fitting is a critical operational practice. For many products, the operational outcome of the retail supply chain is determined by the customer physically fitting products. Digital product fitting is an emerging operational practice in retail that uses digital models of products and customers to match product supply to customer requirements. This paper aims to explore potential supply chain outcomes of digitalizing the operational practice of product fitting. The purpose is to explore and propose the potential of the practice to improve responsiveness to customer requirements and the utilization of existing variety in mass-produced products.

A maturity model of product fitting is developed to specify three levels of digitalization and potential outcomes for each level. Potential outcomes are developed based on empirical data from a case survey of three technology-developing companies, 13 retail cases and a review of academic literature.

With increasing maturity of digital product fitting, the practice can be used for more purposes. Besides matching product supply to customer demand, the practice can improve material flows, customer relationship management, assortment planning and product development. The practice of digital product fitting is most relevant for products where the final product configuration is difficult to make to order, product and customer attributes are easily measurable and tacit knowledge of customers and products can be formalized using digital modeling.

Potential outcomes are conceptualized and proposed. Further research is needed to observe actual outcomes and understand the mechanisms for both proposed and surprising outcomes in specific contexts.

The maturity model helps companies assess how their operations can benefit from digital product fitting and the efforts required to achieve beneficial outcomes.

This paper is a first attempt to describe the potential outcomes of introducing digital product fitting in retail supply chains.

Alzheimer’s is the most commonly occurring neurodegenerative disease and progressive cognitive impairment is its major symptom due to which the patients tend to wander and get lost in unfamiliar places. This is a constant cause of worry for caretakers and a source of distress to the patients themselves. The paper aims to discuss this issue.

This paper presents a low-cost, autonomous, embedded systems-based wearable device for real-time location tracking using GPS and the concept of geo-fencing. The system provides real-time updates in the form of a text message sent to the mobile number of a family member or caregiver.

An alert is sent whenever the patient moves out of a certain “safe zone” area and sends subsequent updates after every 5 min of such an event. The system supports caregivers of patients with early and moderate Alzheimer’s disease.

Alzheimer’s patients are prone to disorientation, confusion and tend to wander off. Since the device eliminates the need for the patients to operate it and is instead at the discretion of the system itself, the chances of it failing to help are minimized. Hence, with this project, the authors address the need for an autonomous device that can assist caretakers in tracking Alzheimer’s patients.

The various existing technologies that are in use now for tracking are often high in price, not tailored to Alzheimer’s and are non-autonomous. To overcome this, the authors utilized easily accessible technology into developing this system, which not only be affordable, but also addresses the major flaw in existing systems – which is that they rely on being operated by the patients themselves.

This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.

In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.

This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.

The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.

This pragmatic research paper aims to unravel the smart vision-based method (SVBM), an AI program to correlate the computer vision (recorded and live videos using mobile and embedded cameras) that aids in manual lifting human pose deduction, analysis and training in the construction sector.

Using a pragmatic approach combined with the literature review, this study discusses the SVBM. The research method includes a literature review followed by a pragmatic approach and lab validation of the acquired data. Adopting the practical approach, the authors of this article developed an SVBM, an AI program to correlate computer vision (recorded and live videos using mobile and embedded cameras).

Results show that SVBM observes the relevant events without additional attachments to the human body and compares them with the standard axis to identify abnormal postures using mobile and other cameras. Angles of critical nodal points are projected through human pose detection and calculating body part movement angles using a novel software program and mobile application. The SVBM demonstrates its ability to data capture and analysis in real-time and offline using videos recorded earlier and is validated for program coding and results repeatability.

Literature review methodology limitations include not keeping in phase with the most updated field knowledge. This limitation is offset by choosing the range for literature review within the last two decades. This literature review may not have captured all published articles because the restriction of database access and search was based only on English. Also, the authors may have omitted fruitful articles hiding in a less popular journal. These limitations are acknowledged. The critical limitation is that the trust, privacy and psychological issues are not addressed in SVBM, which is recognised. However, the benefits of SVBM naturally offset this limitation to being adopted practically.

The theoretical and practical implications include customised and individualistic prediction and preventing most posture-related hazardous behaviours before a critical injury happens. The theoretical implications include mimicking the human pose and lab-based analysis without attaching sensors that naturally alter the working poses. SVBM would help researchers develop more accurate data and theoretical models close to actuals.

By using SVBM, the possibility of early deduction and prevention of musculoskeletal disorders is high; the social implications include the benefits of being a healthier society and health concerned construction sector.

Human pose detection, especially joint angle calculation in a work environment, is crucial to early deduction of muscoloskeletal disorders. Conventional digital technology-based methods to detect pose flaws focus on location information from wearables and laboratory-controlled motion sensors. For the first time, this paper presents novel computer vision (recorded and live videos using mobile and embedded cameras) and digital image-related deep learning methods without attachment to the human body for manual handling pose deduction and analysis of angles, neckline and torso line in an actual construction work environment.