Daily Physical Activity Classification using a Head-mounted device

Cristiano, Alessia; Sanna, Alberto; Trojaniello, Diana

doi:10.1109/ice.2019.8792621

Cited by 3 publications

(6 citation statements)

References 20 publications

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“…The best classification performances were obtained with KNN (95.62%) [28], SVM (94.80%) [28], DT (92.04%) [28] or RF (91.04%) techniques [80]. In cases with a large training database (data acquired from more than 10 people), we observed that the accuracy was in the range of 89% [23] to 98.61% [36]. For this case, the best classification performances were obtained using RF (98.61%) [36], DT (97.57%) [36], cropped random forest (98.56%) [36], or Gaussian mixture models (92%) [25].…”

Section: Classical Machine Learning Modelsmentioning

confidence: 84%

“…A common example is the detection of daily activities (e.g., sitting, standing, walking, etc.) [22,23], medical assistance (e.g., upper limb disabilities, elderly care, etc.) [24,25], or focusing on the understanding of head motion patterns [26,27].…”

Section: Literature Review Methodsmentioning

confidence: 99%

“…Another study area is characterized by the identification of daily activities. Such a system was proposed by Cristiano et al [23]. Their system contained a 6DOF inertial sensor and was designed to identify static and dynamic body activities.…”

Section: Head Motion Systemsmentioning

confidence: 99%

“…In the case of lower acquisition frequency, around 10-20 Hz, the proposed studies focused on determining head motion patterns, which has applicability in medical fields [28,40]. The most common acquisition frequency was observed to be within a range of 48 Hz to 100 Hz, with diverse applicability (general HCI solutions, physical and mental activity analyses, sports training, and medical) [23,36,41,42,47,62,[64][65][66]. Another range which we determined had been used in the papers reviewed was an acquisition frequency of 120 Hz to 48 kHz.…”

Section: Head Motion Systemsmentioning

confidence: 99%

“…The reported results depend on the complexity of head motion activity and the acquisition period. In the data acquisition period, this process can take from a few seconds or minutes [23,66,77] to the real condition period (15 min [24,60], half an hour or one hour [41,47,70], 3 days [56], or 15 weeks [52]).…”

Section: Classical Machine Learning Modelsmentioning

confidence: 99%

See 4 more Smart Citations

Using Inertial Sensors to Determine Head Motion—A Review

Severin

Dobrea

2021

J. Imaging

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Human activity recognition and classification are some of the most interesting research fields, especially due to the rising popularity of wearable devices, such as mobile phones and smartwatches, which are present in our daily lives. Determining head motion and activities through wearable devices has applications in different domains, such as medicine, entertainment, health monitoring, and sports training. In addition, understanding head motion is important for modern-day topics, such as metaverse systems, virtual reality, and touchless systems. The wearability and usability of head motion systems are more technologically advanced than those which use information from a sensor connected to other parts of the human body. The current paper presents an overview of the technical literature from the last decade on state-of-the-art head motion monitoring systems based on inertial sensors. This study provides an overview of the existing solutions used to monitor head motion using inertial sensors. The focus of this study was on determining the acquisition methods, prototype structures, preprocessing steps, computational methods, and techniques used to validate these systems. From a preliminary inspection of the technical literature, we observed that this was the first work which looks specifically at head motion systems based on inertial sensors and their techniques. The research was conducted using four internet databases—IEEE Xplore, Elsevier, MDPI, and Springer. According to this survey, most of the studies focused on analyzing general human activity, and less on a specific activity. In addition, this paper provides a thorough overview of the last decade of approaches and machine learning algorithms used to monitor head motion using inertial sensors. For each method, concept, and final solution, this study provides a comprehensive number of references which help prove the advantages and disadvantages of the inertial sensors used to read head motion. The results of this study help to contextualize emerging inertial sensor technology in relation to broader goals to help people suffering from partial or total paralysis of the body.

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Section: Classical Machine Learning Modelsmentioning

confidence: 84%

Section: Literature Review Methodsmentioning

confidence: 99%

Section: Head Motion Systemsmentioning

confidence: 99%

Section: Head Motion Systemsmentioning

confidence: 99%

Section: Classical Machine Learning Modelsmentioning

confidence: 99%

See 3 more Smart Citations

Using Inertial Sensors to Determine Head Motion—A Review

Severin

Dobrea

2021

J. Imaging

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Development of a Smart Wearable Device for Real-Time Activity Detection

Lyu

2022

2022 IEEE 8th International Conference on Computer and Communications (ICCC)

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TMCA-Net: A Compact Convolution Network for Monitoring Upper Limb Rehabilitation

Liu¹,

Wu²,

Liu³

2022

Journal on Internet of Things

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This study proposed a lightweight but high-performance convolution network for accurately classifying five upper limb movements of arm, involving forearm flexion and rotation, arm extension, lumbar touch and no reaction state, aiming to monitoring patient's rehabilitation process and assist the therapist in elevating patient compliance with treatment. To achieve this goal, a lightweight convolution neural network TMCA-Net (Time Multiscale Channel Attention Convolutional Neural Network) is designed, which combines attention mechanism, uses multi-branched convolution structure to automatically extract feature information at different scales from sensor data, and filters feature information based on attention mechanism. In particular, channel separation convolution is used to replace traditional convolution. This reduces the computational complexity of the model, decouples the convolution operation of the time dimension and the cross-channel feature interaction, which is helpful to the target optimization of feature extraction. TMCA-Net shows excellent performance in the upper limb rehabilitation gesture data, achieves 99.11% accuracy and 99.16% F1-score for the classification and recognition of five gestures. Compared with CNN and LSTM network, it achieves 65.62% and 89.98% accuracy in the same task. In addition, on the UCI smartphone public dataset, with the network parameters of one tenth of the current advanced model, the recognition accuracy rate of 95.21% has been achieved, which further proves the light weight and performance characteristics of the model. The clinical significance of this study is to accurately monitor patients' upper limb rehabilitation gesture by an affordable intelligent model as an auxiliary support for therapists' decision-making.

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Daily Physical Activity Classification using a Head-mounted device

Cited by 3 publications

References 20 publications

Using Inertial Sensors to Determine Head Motion—A Review

Using Inertial Sensors to Determine Head Motion—A Review

Development of a Smart Wearable Device for Real-Time Activity Detection

TMCA-Net: A Compact Convolution Network for Monitoring Upper Limb Rehabilitation

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