A Haptic Feedback System for Lower Limb Amputees Based on Gait Event Detection

Lower limb amputation has significant effects on a person’s quality of life and ability to perform activities of daily living. Prescription of prosthetic device post amputation aims to help restore some degrees of mobility function, however studies have shown evidence of low balance confidence and higher risk of falling among amputee community, especially those suffering from above knee amputation. While advanced prostheses offer better control, they often lack a form of feedback that delivers the awareness of the limb position to the prosthetic user while walking. This research presents the development and evaluation of a wearable skinstretch haptic feedback system intended to deliver cues of two crucial gait events, namely the Initial Contact (IC) and Toe-off (TO) to its wearer. The system comprises a haptic module that applies lateral skin-stretch on the upper leg or the trunk, corresponding to the gait event detection module based on Inertial Measurement Unit (IMU) attached at the shank. The design and development iterations of the haptic module is presented, and characterization of the feedback parameters is discussed. The validation of the gait event detection module is carried out and finally the integration of the haptic feedback system is described. Experimental work with healthy subjects and an amputee indicated good perceptibility of the feedback during static and dynamic (walking) condition, although higher magnitude of stretch was required to perceive the feedback during dynamic condition. User response time during dynamic activity showed that the haptic feedback system is suitable for delivering cues of IC and TO within the duration of the stance phase. In addition, feedback delivered in discernible patterns can be learned and adapted by the subjects. Finally, a case study was carried out with an above-knee amputee to assess the effects of the haptic feedback on spatio-temporal gait parameters and on the vertical ground reaction force during treadmill and overground walking. The research presented in this report introduces a novel design of a haptic feedback device. As such, the outcome includes a well-controlled skin-stretch effect which contributes to the research by investigating skin-stretch feedback for conveying discrete event information rather than conveying direction information as presented in other studies. In addition, it is found that stretch magnitude as small as 3 mm could be perceived in short duration of 150 ms during dynamic condition, making it a suitable alternative to other widely investigated haptic modality such as vibration for ambulatory feedback application. With continuous training, the haptic feedback system could possibly benefit lower limb amputees by creating awareness of the limb placement during ambulation, potentially reducing visual dependency and increasing walking confidence

Portable haptic device for lower limb amputee gait feedback: assessing static and dynamic perceptibility

Loss of joints and severed sensory pathway cause reduced mobility capabilities in lower limb amputees. Although prosthetic devices attempt to restore normal mobility functions, lack of awareness and control of limb placement increase the risk of falling and causing amputee to have high level of visual dependency. Haptic feedback can serve as a cue for gait events during ambulation thus providing sense of awareness of the limb position. This paper presents a wireless wearable skin stretch haptic device to be fitted around the thigh region. The movement profile of the device was characterized and a preliminary work with able-bodied participants and an above-knee amputee to assess the ability of users to perceive the delivered stimuli during static and dynamic mode is reported. Perceptibility was found to be increasing with stretch magnitude. It was observed that a higher magnitude of stretch was needed for the stimuli to be accurately perceived during walking in comparison to static standing, most likely due to the intense movement of the muscle and increased motor skills demand during walking activity

Unmanned aerial vehicles for crowd monitoring and analysis

Crowd monitoring and analysis has become increasingly used for unmanned aerial vehicle applications. From preventing stampede in high concentration crowds to estimating crowd density and to surveilling crowd movements, crowd monitoring and analysis have long been employed in the past by authorities and regulatory bodies to tackle challenges posed by large crowds. Conventional methods of crowd analysis using static cameras are limited due to their low coverage area and non-flexible perspectives and features. Unmanned aerial vehicles have tremendously increased the quality of images obtained for crowd analysis reasons, relieving the relevant authorities of the venues’ inadequacies and of concerns of inaccessible locations and situation. This paper reviews existing literature sources regarding the use of aerial vehicles for crowd monitoring and analysis purposes. Vehicle specifications, onboard sensors, power management, and an analysis algorithm are critically reviewed and discussed. In addition, ethical and privacy issues surrounding the use of this technology are presented

Efficient region of interest based metric learning for effective open world deep face

Face Recognition (FR) has recently gained traction as a widely used biometric for securitybased applications such as facial recognition payment. The widespread use is due to improvements in deep convolutional neural networks (CNN) and large datasets. However, FR is still an ill-posed problem, especially in an open world scenario. Existing FR methods require finetuning, classifier retraining, or global metric learning to improve the performance for effective domain adaptation. It incurs an undesirable downtime. Open world FR must identify the persons for whom the FR model is not trained. It also produces imbalanced pairs, giving a false sense of high performance. The popular fixed threshold strategies, such as σ values, also lead to sub-optimal performance. This paper proposes a fast and efficient threshold adapter algorithm using an effective Region of Interest (ROI) setting for metric learning. It uses five different ROI schemes to find an adaptive threshold in real-time. The algorithm also determines the FR model quality and usability after new enrolments. To establish the effectiveness, we investigated various threshold finding strategies for five state-of-the-art face recognition algorithms for open world adaptation on different datasets.We also proposed a novel performance evaluation metric for FR algorithms on imbalanced datasets. Experimental results demonstrated that the proposed metric learning is up to 12 times faster than the nearest competitor while reporting higher accuracy and fewer errors. The study suggests that the F1-score is vital as a performance indicator for imbalanced pair evaluation, and accuracy at the highest reported F1-score is the desired metric for benchmarking FR algorithms in open world

Design of smart shoes for blind people

Our daily lives depend heavily on our eyes. Eyesight is our most valuable gift, enabling us to see the world around us. However, some people suffer from visual impairments that hinder their ability to visualize such things. As a result, such people will experience difficulties moving comfortably in public places. One crucial aspect of mobile accessibility is detecting elevation changes. These include changes in the height of the ground or a floor, such as stairs, curbing, and potholes. They are common in both indoor and outdoor environments. People who are blind or visually impaired must detect these changes and assess their distance and extent to navigate them safely and effectively. Depth perception is essential to doing so and can be challenging for those with visual impairments. Therefore, this research aims to design a smart shoe that assists in climbing up and down the stairs using an IMU sensor to detect the user's movement. Before constructing a controller, the system is modelled using mathematical and physical modelling. Mathematical modelling is derived based on the mobility of people with visual impairment. The smart shoes are modelled in a 3D virtual world using the SolidWorks software. In addition, the shoe integrates with ultrasonic sensors whenever it detects any obstacles or barriers; they alert the users via vibration. This resulted in the intelligent shoes unlocking the heels whenever the low or high elevation was detected and vibrating if there was an obstacle. With the help of this device, the confidence level of people with visual impairment to walk independently will be improved

A method for preserving battery life in wireless sensor nodes for LoRa based IOT flood monitoring

A common challenge in the implementation of Internet of Things (IOT) Wireless Sensor Networks (WSN) is that the sensor nodes are known to be power hungry devices. The energy stored in the batteries which power up these sensor nodes deplete quickly especially when more data is transmitted to the cloud or when multiple sensors are attached to a single sensor node. In the context of flood and environmental monitoring, increasing the number of sensor nodes in a Wireless Sensor Network is desirable in order to increase the spatial resolution of the data and hence achieve better representation about rising water levels and overall water quality in a particular city. However, having more sensor nodes in a Wireless Sensor Network results in more challenges for the power supply management of the overall Wireless Sensor Network. A drawback of the sensor nodes which are usually powered by Lithium Ion batteries is that there is a limited number of cycles in which a battery can be charged and discharged before the battery is considered to be fully degraded and therefore methods which can lengthen the duration of each charging and discharging cycle will be useful to increase the overall battery longevity. In this paper, a method which combines solar energy harvesting together with a fuzzy logic based algorithm for adaptive sampling is proposed in order to achieve a continuous source of energy for the sensor nodes and also increase the duration between each charging and discharging cycle resulting in batteries which can last for a longer duration. The developed sensor nodes have been deployed to measure river water levels and dissolved oxyge

Towards the implementation of energy harvesting for IoT sensor nodes in an early warning flood detection system

Flood sensors are deployed to measure the water level of rivers in areas prone to flooding. Flood is a frequent event in many places and the after effect almost always results in loss of properties and lives. Deploying sensors in remote areas to provide significant benefit in mitigating the after-effects of flood, however, it is not simple as the nodes would require a constant source of clean energy. This research explores the potentials of energy harvesting as a means for the sensor nodes to be self-sustaining by using a clean source of energy in order to achieve constant monitoring of water levels in remote flood-prone areas

Pavement condition analysis via vehicle mounted accelerometer data

Road anomalies and irregularities such as potholes and uneven surfaces are a common hazard in South East Asia and developing countries. Such hazards pose a threat to the safety and well-being of both civilians going about their daily routine and tourists who are exploring the city. Since bicycles and rickshaws are still a common mode of transport used by both civilians and tourists in many South East Asian countries, it is essential to improve the overall quality and smoothness of pavements which are traversed by these vehicles. Management of international sporting and recreational events also require satisfactory road and pavement conditions. Before pavement conditions can be improved, it is an essential prerequisite to obtain comprehensive information about road irregularities such as the location and also severity of the road irregularity (depth of the potholes and height of bumps). In this paper, we propose a method for obtaining mathematical models that represent the overall condition of the pavements that are part of a commonly traversed cycling route. Such mathematical models and coefficients can be stored in the cloud of an Internet of Things (IOT) data analytics systems subsequently leading to identification of regions with severe road irregularities

Mechatronics Engineering Department Newsletter - Risalah Issue 1 October 2021

‘MECHATRONICS ENGINEERING DEPARTMENT NEWSLETTER - RISALAH ISSUE 1 is the first bi-yearly newsletter produced by the Mechatronics Engineering Department in Kulliyyah of Engineering, IIUM featuring various events, announcements and information regarding the department including key events, research projects, student activities and success stories in the department