A Multi-Sensor Matched Filter Approach to Robust Segmentation of Assisted Gait

User Authentication by Gait Data from Smartphone Sensors Using Hybrid Deep Learning Network

User authentication and verification by gait data based on smartphones’ inertial sensors has gradually attracted increasing attention due to their compact size, portability and affordability. However, the existing approaches often require users to walk on a specific road at a normal walking speed to improve recognition accuracy. In order to recognize gaits under unconstrained conditions on where and how users walk, we proposed a Hybrid Deep Learning Network (HDLN), which combined the advantages of a long short-term memory (LSTM) network and a convolutional neural network (CNN) to reliably extract discriminative features from complex smartphone inertial data. The convergence layer of HDLN was optimized through a spatial pyramid pooling and attention mechanism. The former ensured that the gait features were extracted from more dimensions, and the latter ensured that only important gait information was processed while ignoring unimportant data. Furthermore, we developed an APP that can achieve real-time gait recognition. The experimental results showed that HDLN achieved better performance improvements than CNN, LSTM, DeepConvLSTM and CNN+LSTM by 1.9%, 2.8%, 2.0% and 1.3%, respectively. Furthermore, the experimental results indicated our model’s high scalability and strong suitability in real application scenes.

Stride segmentation of inertial sensor data using statistical methods for different walking activities

Human gait data can be collected using inertial measurement units (IMUs). An IMU is an electronic device that uses an accelerometer and gyroscope to capture three-axial linear acceleration and three-axial angular velocity. The data so collected are time series in nature. The major challenge associated with these data is the segmentation of signal samples into stride-specific information, that is, individual gait cycles. One empirical approach for stride segmentation is based on timestamps. However, timestamping is a manual technique, and it requires a timing device and a fixed laboratory set-up which usually restricts its applicability outside of the laboratory. In this study, we have proposed an automatic technique for stride segmentation of accelerometry data for three different walking activities. The autocorrelation function (ACF) is utilized for the identification of stride boundaries. Identification and extraction of stride-specific data are done by devising a concept of tuning parameter ( $t_{p}$ ) which is based on minimum standard deviation ( $\sigma$ ). Rigorous experimentation is done on human activities and postural transition and Osaka University – Institute of Scientific and Industrial Research gait inertial sensor datasets. Obtained mean stride duration for level walking, walking upstairs, and walking downstairs is 1.1, 1.19, and 1.02 s with 95% confidence interval [1.08, 1.12], [1.15, 1.22], and [0.97, 1.07], respectively, which is on par with standard findings reported in the literature. Limitations of accelerometry and ACF are also discussed. stride segmentation; human activity recognition; accelerometry; gait parameter estimation; gait cycle; inertial measurement unit; autocorrelation function; wearable sensors; IoT; edge computing; tinyML.

Generation of Gait Events with a FSR Based Cane Handle

Gait analysis has many applications, and specifically can improve the control of prosthesis, exoskeletons, or Functional Electrical Stimulation systems. The use of canes is common to complement the assistance in these cases, and the synergy between upper and lower limbs can be exploited to obtain information about the gait. This is interesting especially in the case of unilateral assistance, for instance in the case of one side lower limb exoskeletons. If the cane is instrumented, it can hold sensors that otherwise should be attached to the body of the impaired user. This can ease the use of the assistive system in daily life as well as its acceptance. Moreover, Force Sensing Resistors (FSRs) are common in gait phase detection systems, and force sensors are also common in user intention detection. Therefore, a cane that incorporates FSRs on the handle can take advantage from the direct interface with the human and provide valuable information to implement real-time control. This is done in this paper, and the results confirm that many events are detected from variables derived from the readings of the FSRs that provide rich information about gait. However, a large inter-subject variability points to the need of tailored control systems.

Inertial Measurement Unit Sensors in Assistive Technologies for Visually Impaired People, a Review

A diverse array of assistive technologies have been developed to help Visually Impaired People (VIP) face many basic daily autonomy challenges. Inertial measurement unit sensors, on the other hand, have been used for navigation, guidance, and localization but especially for full body motion tracking due to their low cost and miniaturization, which have allowed the estimation of kinematic parameters and biomechanical analysis for different field of applications. The aim of this work was to present a comprehensive approach of assistive technologies for VIP that include inertial sensors as input, producing results on the comprehension of technical characteristics of the inertial sensors, the methodologies applied, and their specific role in each developed system. The results show that there are just a few inertial sensor-based systems. However, these sensors provide essential information when combined with optical sensors and radio signals for navigation and special application fields. The discussion includes new avenues of research, missing elements, and usability analysis, since a limitation evidenced in the selected articles is the lack of user-centered designs. Finally, regarding application fields, it has been highlighted that a gap exists in the literature regarding aids for rehabilitation and biomechanical analysis of VIP. Most of the findings are focused on navigation and obstacle detection, and this should be considered for future applications.

Hardware for Recognition of Human Activities: A Review of Smart Home and AAL Related Technologies

Activity recognition (AR) from an applied perspective of ambient assisted living (AAL) and smart homes (SH) has become a subject of great interest. Promising a better quality of life, AR applied in contexts such as health, security, and energy consumption can lead to solutions capable of reaching even the people most in need. This study was strongly motivated because levels of development, deployment, and technology of AR solutions transferred to society and industry are based on software development, but also depend on the hardware devices used. The current paper identifies contributions to hardware uses for activity recognition through a scientific literature review in the Web of Science (WoS) database. This work found four dominant groups of technologies used for AR in SH and AAL-smartphones, wearables, video, and electronic components-and two emerging technologies: Wi-Fi and assistive robots. Many of these technologies overlap across many research works. Through bibliometric networks analysis, the present review identified some gaps and new potential combinations of technologies for advances in this emerging worldwide field and their uses. The review also relates the use of these six technologies in health conditions, health care, emotion recognition, occupancy, mobility, posture recognition, localization, fall detection, and generic activity recognition applications. The above can serve as a road map that allows readers to execute approachable projects and deploy applications in different socioeconomic contexts, and the possibility to establish networks with the community involved in this topic. This analysis shows that the research field in activity recognition accepts that specific goals cannot be achieved using one single hardware technology, but can be using joint solutions, this paper shows how such technology works in this regard.

Comparison of Feedback Approaches to Improve Training in Partial Weight-Bearing

Assistive devices, including canes or crutches, are used in partial weight-bearing (PWB) to offload weight from limbs weakened by disease or injury, promote recovery, and prevent reinjury. While weight must be offloaded accurately to target loads prescribed by healthcare providers for maximum benefit, current training methods result in poor adherence. It is, however, currently unknown how best to provide feedback during training so that users can build an accurate internal model for PWB. In this work, we investigate seven feedback schemes using an instrumented cane, which vary the modality, timing, and the level of detail provided. We find that auditory schemes and a retrospective visual scheme outperform current clinical practices for PWB training. These findings provide results that can be applied directly to improve current clinical practices and provide valuable new insight into the design of feedback for training internal models in force-based motor control tasks. Clinically, this work presents a simple modification to clinical PWB training practices that can improve compliance by up to 75%, positively influencing rehabilitation outcomes and reducing the risk of complications.

A Multi-Sensor Cane Can Detect Changes in Gait Caused by Simulated Gait Abnormalities and Walking Terrains

Due to the increasing rates of chronic diseases and an aging population, the use of assistive devices for ambulation is expected to grow rapidly over the next several years. Instrumenting these devices has been proposed as a non-invasive way to proactively monitor changes in gait due to the presence of pain or a condition in outdoor and indoor environments. In this paper, we evaluated the effectiveness of a multi-sensor cane in detecting changes in gait due to the presence of simulated gait abnormalities, walking terrains, impaired vision, and incorrect cane lengths. The effectiveness of the instrumented cane was compared with the results obtained directly from a shank-mounted inertial measurement unit. Results from 30 healthy participants obtained while simulating gait abnormalities and walking over different terrains demonstrated the ability of the cane to reliably and effectively discriminate among these walking conditions. Moreover, the results obtained while walking with impaired vision and incorrect cane lengths indicate the ability of cane to detect changes in gait during these scenarios as well.