Research

17 pages, 6086 KiB

Open AccessArticle

Novel Deep Learning Network for Gait Recognition Using Multimodal Inertial Sensors

by Ling-Feng Shi, Zhong-Ye Liu, Ke-Jun Zhou, Yifan Shi and Xiao Jing

Sensors 2023, 23(2), 849; https://0-doi-org.brum.beds.ac.uk/10.3390/s23020849 - 11 Jan 2023

Cited by 14 | Viewed by 2917

Some recent studies use a convolutional neural network (CNN) or long short-term memory (LSTM) to extract gait features, but the methods based on the CNN and LSTM have a high loss rate of time-series and spatial information, respectively. Since gait has obvious time-series [...] Read more.

Some recent studies use a convolutional neural network (CNN) or long short-term memory (LSTM) to extract gait features, but the methods based on the CNN and LSTM have a high loss rate of time-series and spatial information, respectively. Since gait has obvious time-series characteristics, while CNN only collects waveform characteristics, and only uses CNN for gait recognition, this leads to a certain lack of time-series characteristics. LSTM can collect time-series characteristics, but LSTM results in performance degradation when processing long sequences. However, using CNN can compress the length of feature vectors. In this paper, a sequential convolution LSTM network for gait recognition using multimodal wearable inertial sensors is proposed, which is called SConvLSTM. Based on 1D-CNN and a bidirectional LSTM network, the method can automatically extract features from the raw acceleration and gyroscope signals without a manual feature design. 1D-CNN is first used to extract the high-dimensional features of the inertial sensor signals. While retaining the time-series features of the data, the dimension of the features is expanded, and the length of the feature vectors is compressed. Then, the bidirectional LSTM network is used to extract the time-series features of the data. The proposed method uses fixed-length data frames as the input and does not require gait cycle detection, which avoids the impact of cycle detection errors on the recognition accuracy. We performed experiments on three public benchmark datasets: UCI-HAR, HuGaDB, and WISDM. The results show that SConvLSTM performs better than most of those reporting the best performance methods, at present, on the three datasets. Full article

(This article belongs to the Special Issue Advanced Sensors/Devices for Ambient Assisted Living)

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19 pages, 3065 KiB

Open AccessArticle

A Comparison among Different Strategies to Detect Potential Unstable Behaviors in Postural Sway

by Bruno Andò, Salvatore Baglio, Salvatore Graziani, Vincenzo Marletta, Valeria Dibilio, Giovanni Mostile and Mario Zappia

Sensors 2022, 22(19), 7106; https://0-doi-org.brum.beds.ac.uk/10.3390/s22197106 - 20 Sep 2022

Cited by 5 | Viewed by 1362

Abstract

Assistive Technology helps to assess the daily living and safety of frail people, with particular regards to the detection and prevention of falls. In this paper, a comparison is provided among different strategies to analyze postural sway, with the aim of detecting unstable [...] Read more.

Assistive Technology helps to assess the daily living and safety of frail people, with particular regards to the detection and prevention of falls. In this paper, a comparison is provided among different strategies to analyze postural sway, with the aim of detecting unstable postural status in standing condition as precursors of potential falls. Three approaches are considered: (i) a time-based features threshold algorithm, (ii) a time-based features Neuro-Fuzzy inference system, and (iii) a Neuro-Fuzzy inference fed by Discrete-Wavelet-Transform-based features. The analysis was performed across a wide dataset and exploited performance indexes aimed at assessing the accuracy and the reliability of predictions provided by the above-mentioned strategies. The results obtained demonstrate valuable performances of the three considered strategies in correctly distinguishing among stable and unstable postural status. However, the analysis of robustness against noisy data highlights better performance of Neuro-Fuzzy inference systems with respect to the threshold-based algorithm. Full article

(This article belongs to the Special Issue Advanced Sensors/Devices for Ambient Assisted Living)

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23 pages, 8353 KiB

Open AccessArticle

Edge-Computing Meshed Wireless Acoustic Sensor Network for Indoor Sound Monitoring

by Selene Caro-Via, Ester Vidaña-Vila, Gerardo José Ginovart-Panisello, Carme Martínez-Suquía, Marc Freixes and Rosa Ma Alsina-Pagès

Sensors 2022, 22(18), 7032; https://0-doi-org.brum.beds.ac.uk/10.3390/s22187032 - 17 Sep 2022

Cited by 2 | Viewed by 1759

Abstract

This work presents the design of a wireless acoustic sensor network (WASN) that monitors indoor spaces. The proposed network would enable the acquisition of valuable information on the behavior of the inhabitants of the space. This WASN has been conceived to work in [...] Read more.

This work presents the design of a wireless acoustic sensor network (WASN) that monitors indoor spaces. The proposed network would enable the acquisition of valuable information on the behavior of the inhabitants of the space. This WASN has been conceived to work in any type of indoor environment, including houses, hospitals, universities or even libraries, where the tracking of people can give relevant insight, with a focus on ambient assisted living environments. The proposed WASN has several priorities and differences compared to the literature: (i) presenting a low-cost flexible sensor able to monitor wide indoor areas; (ii) balance between acoustic quality and microphone cost; and (iii) good communication between nodes to increase the connectivity coverage. A potential application of the proposed network could be the generation of a sound map of a certain location (house, university, offices, etc.) or, in the future, the acoustic detection of events, giving information about the behavior of the inhabitants of the place under study. Each node of the network comprises an omnidirectional microphone and a computation unit, which processes acoustic information locally following the edge-computing paradigm to avoid sending raw data to a cloud server, mainly for privacy and connectivity purposes. Moreover, this work explores the placement of acoustic sensors in a real scenario, following acoustic coverage criteria. The proposed network aims to encourage the use of real-time non-invasive devices to obtain behavioral and environmental information, in order to take decisions in real-time with the minimum intrusiveness in the location under study. Full article

(This article belongs to the Special Issue Advanced Sensors/Devices for Ambient Assisted Living)

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23 pages, 22898 KiB

Open AccessArticle

Ambient and Wearable Sensor Technologies for Energy Expenditure Quantification of Ageing Adults

by Alessandro Leone, Gabriele Rescio, Giovanni Diraco, Andrea Manni, Pietro Siciliano and Andrea Caroppo

Sensors 2022, 22(13), 4893; https://0-doi-org.brum.beds.ac.uk/10.3390/s22134893 - 29 Jun 2022

Cited by 5 | Viewed by 1954

Abstract

COVID-19 has affected daily life in unprecedented ways, with dramatic changes in mental health, sleep time and level of physical activity. These changes have been especially relevant in the elderly population, with important health-related consequences. In this work, two different sensor technologies were [...] Read more.

COVID-19 has affected daily life in unprecedented ways, with dramatic changes in mental health, sleep time and level of physical activity. These changes have been especially relevant in the elderly population, with important health-related consequences. In this work, two different sensor technologies were used to quantify the energy expenditure of ageing adults. To this end, a technological platform based on Raspberry Pi 4, as an elaboration unit, was designed and implemented. It integrates an ambient sensor node, a wearable sensor node and a coordinator node that uses the information provided by the two sensor technologies in a combined manner. Ambient and wearable sensors are used for the real-time recognition of four human postures (standing, sitting, bending and lying down), walking activity and for energy expenditure quantification. An important first aim of this work was to realize a platform with a high level of user acceptability. In fact, through the use of two unobtrusive sensors and a low-cost processing unit, the solution is easily accessible and usable in the domestic environment; moreover, it is versatile since it can be used by end-users who accept being monitored by a specific sensor. Another added value of the platform is the ability to abstract from sensing technologies, as the use of human posture and walking activity for energy expenditure quantification enables the integration of a wide set of devices, provided that they can reproduce the same set of features. The obtained results showed the ability of the proposed platform to automatically quantify energy expenditure, both with each sensing technology and with the combined version. Specifically, for posture and walking activity classification, an average accuracy of 93.8% and 93.3% was obtained, respectively, with the wearable and ambient sensor, whereas an improvement of approximately 4% was reached using data fusion. Consequently, the estimated energy expenditure quantification always had a relative error of less than 3.2% for each end-user involved in the experimentation stage, classifying the high level information (postures and walking activities) with the combined version of the platform, justifying the proposed overall architecture from a hardware and software point of view. Full article

(This article belongs to the Special Issue Advanced Sensors/Devices for Ambient Assisted Living)

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16 pages, 2634 KiB

Open AccessArticle

Estimation of Steering and Throttle Angles of a Motorized Mobility Scooter with Inertial Measurement Units for Continuous Quantification of Driving Operation

by Jun Suzurikawa, Shunsuke Kurokawa, Haruki Sugiyama and Kazunori Hase

Sensors 2022, 22(9), 3161; https://0-doi-org.brum.beds.ac.uk/10.3390/s22093161 - 20 Apr 2022

Cited by 2 | Viewed by 2550

Abstract

With the growing demand from elderly persons for alternative mobility solutions, motorized mobility scooters (MMSs) have been gaining importance as an essential assistive technology to aid independent living in local communities. The increased use of MMSs, however, has raised safety issues during driving [...] Read more.

With the growing demand from elderly persons for alternative mobility solutions, motorized mobility scooters (MMSs) have been gaining importance as an essential assistive technology to aid independent living in local communities. The increased use of MMSs, however, has raised safety issues during driving and magnified the necessity to evaluate and improve user driving skills. This study is intended to develop a novel quantitative monitoring method for MMS driving operation using inertial measurement units (IMUs). The proposed method used coordinate transformations around the rotational axes of the steering wheel and the throttle lever to estimate the steering and throttle operating angles based on gravitational accelerations measured by IMUs. Consequently, these operating angles can be monitored simply using an IMU attached to the throttle lever. Validation experiments with a test MMS in the stationary state confirmed the consistency of the proposed coordinate transformation with the MMS’s geometrical structure. The driving test also demonstrated that the operating angles were estimated correctly on various terrains and that the effects of terrain inclination were compensated using an additional IMU attached to the scooter body. This method will be applicable to the quantitative monitoring of driving behavior and act as a complementary tool for the existing skills’ evaluation methods. Full article

(This article belongs to the Special Issue Advanced Sensors/Devices for Ambient Assisted Living)

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21 pages, 1070 KiB

Open AccessArticle

Discovering Daily Activity Patterns from Sensor Data Sequences and Activity Sequences

by Mirjam Sepesy Maučec and Gregor Donaj

Sensors 2021, 21(20), 6920; https://0-doi-org.brum.beds.ac.uk/10.3390/s21206920 - 19 Oct 2021

Cited by 6 | Viewed by 2161

Abstract

The necessity of caring for elderly people is increasing. Great efforts are being made to enable the elderly population to remain independent for as long as possible. Technologies are being developed to monitor the daily activities of a person to detect their state. [...] Read more.

The necessity of caring for elderly people is increasing. Great efforts are being made to enable the elderly population to remain independent for as long as possible. Technologies are being developed to monitor the daily activities of a person to detect their state. Approaches that recognize activities from simple environment sensors have been shown to perform well. It is also important to know the habits of a resident to distinguish between common and uncommon behavior. In this paper, we propose a novel approach to discover a person’s common daily routines. The approach consists of sequence comparison and a clustering method to obtain partitions of daily routines. Such partitions are the basis to detect unusual sequences of activities in a person’s day. Two types of partitions are examined. The first partition type is based on daily activity vectors, and the second type is based on sensor data. We show that daily activity vectors are needed to obtain reasonable results. We also show that partitions obtained with generalized Hamming distance for sequence comparison are better than partitions obtained with the Levenshtein distance. Experiments are performed with two publicly available datasets. Full article

(This article belongs to the Special Issue Advanced Sensors/Devices for Ambient Assisted Living)

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15 pages, 3021 KiB

Open AccessArticle

Tom Pouce III, an Electronic White Cane for Blind People: Ability to Detect Obstacles and Mobility Performances

by Aya Dernayka, Michel-Ange Amorim, Roger Leroux, Lucas Bogaert and René Farcy

Sensors 2021, 21(20), 6854; https://0-doi-org.brum.beds.ac.uk/10.3390/s21206854 - 15 Oct 2021

Cited by 4 | Viewed by 2069

Abstract

We present a protocol for evaluating the efficiency of an electronic white cane for improving the mobility of blind people. The electronic cane used during the test is the Tom Pouce III, made of LIDAR sensors (light detection and ranging) with tactile feedback. [...] Read more.

We present a protocol for evaluating the efficiency of an electronic white cane for improving the mobility of blind people. The electronic cane used during the test is the Tom Pouce III, made of LIDAR sensors (light detection and ranging) with tactile feedback. The protocol comprises two parts. The first part, the “detection test”, evaluates the efficiency of the sensors in the Tom Pouce III for detecting the obstacles found in everyday life (thin and large poles, apertures) under different environmental conditions (darkness, sun light, rain). The second part of the test, the “mobility test”, compares the ability of blind participants to cross a 25 m path by avoiding obstacles with the simple white cane and the electronic cane. The 12 blind participants had between 2 and 20 years of experience of everyday usage of Tom Pouce devices. The results show a significant improvement in the capacity to avoid obstacles with the electronic cane relative to the simple white cane, and there was no speed difference. There was no correlation between the results and the years of experience of the users. Full article

(This article belongs to the Special Issue Advanced Sensors/Devices for Ambient Assisted Living)

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22 pages, 4948 KiB

Open AccessArticle

Self-Organizing IoT Device-Based Smart Diagnosing Assistance System for Activities of Daily Living

by Yu Jin Park, Seol Young Jung, Tae Yong Son and Soon Ju Kang

Sensors 2021, 21(3), 785; https://0-doi-org.brum.beds.ac.uk/10.3390/s21030785 - 25 Jan 2021

Cited by 2 | Viewed by 2547

Abstract

Activity of daily living (ADL) is a criterion for evaluating the performance ability of daily life by recognizing various activity events occurring in real life. However, most of the data necessary for ADL evaluation are collected only through observation and questionnaire by the [...] Read more.

Activity of daily living (ADL) is a criterion for evaluating the performance ability of daily life by recognizing various activity events occurring in real life. However, most of the data necessary for ADL evaluation are collected only through observation and questionnaire by the patient or the patient’s caregiver. Recently, Internet of Things (IoT) device studies using various environmental sensors are being used for ADL collection and analysis. In this paper, we propose an IoT Device Platform for ADL capability measurement. Wearable devices and stationary devices recognize activity events in real environments and perform user identification through various sensors. The user’s ADL data are sent to the network hub for analysis. The proposed IoT platform devices support many sensor devices such as acceleration, flame, temperature, and humidity in order to recognize various activities in real life. In addition, in this paper, using the implemented platform, ADL measurement test was performed on hospital patients. Through this test, the accuracy and reliability of the platform are analyzed. Full article

(This article belongs to the Special Issue Advanced Sensors/Devices for Ambient Assisted Living)

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