Abnormal Gait and Tremor Detection in the Elderly Ambulatory Behavior Using an IoT Smart Cane Device
Abstract
:1. Introduction
1.1. Gait Abnormality and Ambulation Pattern
1.2. Elderly Activities Evaluation
1.3. Overview of This Paper
2. Related Work
Drawback of the Existing Methods
3. Material and Methods
3.1. Tremor Detection
3.2. Perception Layer
3.3. Storage Layer
3.4. Smart Learning Platform
4. Activities Simulation
4.1. Filtering and Noise Removal
4.2. Feature Extraction
4.3. Energy Feature
4.4. Dimension Reduction with PCA
4.5. Classification Using LDA
5. Anomaly Detection in Gait Ambulation
- Activity begin time;
- Activity end time;
- Number of steps.
- Pause begin time;
- Pause end time.
- Fall time;
- Fall alert (false when cancel otherwise true).
Data Description
- Date: This represents the day column and the day the activity was carried out;
- Step count: The total number of steps taken by the user on a particular day;
- Pause duration: The total duration of a pause by the user during activity, represented in seconds;
- Mood: This is a heuristic feature that intends to express the user’s feeling on a particular day. Although this is assumed not to be 100 percent accurate, it might be interesting to estimate the user’s daily mood. It turns out that an average person has a step of approximately 2.1 to 2.5 feet. Therefore, it takes approximately 2200 steps to walk one mile, and a step count of 1000 will cover about 762 m, so if an aged person (who is mentally stable) can take about 3000 steps on a particular day, which is more than a one-mile walk, then he/she is assumed to be in a great shape and excellent mood. It is computed based on the following algorithm;
- walk_duration = []
- for i in range (0, len(walk_duration)):
- if walk_duration >= 3000:
- mood is ’Excellent Mood’
- elif walk_duration >= 1000:
- mood is ’Very good mood’ else:
- mood is ’moody’Mood is represented in binary as follows:100 = moody, 200 = Very good mood, and 300 = Excellent Mood.
- Activity length: This is the beginning of a daily activity literately when the cane is picked up.
- Activity end: This is the end of a daily activity literately when the cane is finally laid to rest and no activation is detected for the rest of the day.
- Activity length: This is the length of total activity for the day. It is computed from the sum of the difference in the activity end time and activity begin time. It is then converted into second(s).
- True falls: A true fall is detected when the cane loses its equilibrium, and balance is not regained within a 15 s interval.
- Walk duration: This can be referred to as total active moments of the day because it represents the total duration of ambulation by the user. It is computed by taking the sum of activity length and subtracting the total pause duration.
- Tiredness: This is the rate of exhaustion that maybe experienced by the user. Tiredness may be due to fatigue, or it may be a sign of physical weakness experienced as one grows older. It can also be an indicator of distress. It is computed by taking the ratio of pause duration to the walk duration. It may be noted that threshold can be set for this, and a distress alert can be generated if tiredness is greater than 1. This is not a good sign because it means the user pauses more often than doing the actual walking, although it may not be 100 percent accurate because the user may pause to talk to people or due to some other reasons.
- Speed: This is the rate of change in distance of the user. We can estimate how fast the user moves, and this is computed by taking the ratio of step count to walk duration.
- False falls: This is a trigger alert when the cane loses its equilibrium, but it is cancelled when the cane is picked up within a 15 s time frame.
- True fall time: This is the time when true falls occur.
- False fall time: This is the time when false falls occur.
6. Anomalies Detection with Isolation Forest and One-Class SVM
6.1. Detecting Anomaly with Isolation Forest (Liu and Ting, 2012)
6.2. Detecting Anomaly with One-Class SVM
7. Discussion
8. Conclusions
8.1. Limitations of the Proposed Approach
8.2. Prospects for Future Research
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
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Category | Description | Recognition Accuracy (%) | Algorithm |
---|---|---|---|
Irregular Arm Swing | Arm movements lacking continuity of occurrence | 89.9 | LDA |
Resting | In a relax state | 97.6 | LDA |
Vibrating Arm | Arm shaking vigorously | 97.8 | LDA |
Exhausted | Tired | 98.1 | LDA |
Normal Walking | Expected state of Walking | 99.7 | LDA |
Slow Walking |
Less than expected state of walking (An average person has a step of approximately 2.1 to 2.5 feet. Therefore, it takes approximately 2200 steps to walk one mile, also it takes 15 to 20 min to accomplish for an average individual according to the Centers of Disease Control and Prevention) | 99.6 | LDA |
Activity Data | Precision | Recall | F1-Score | |
---|---|---|---|---|
Vibrating Arm | 0 | 0.98 | 0.98 | 0.98 |
1 | 0.03 | 0.03 | 0.02 | |
Accuracy | 0.97 | |||
Micro Avg | 0.89 | 0.89 | 0.89 | 0.99 |
Weighted Avg | 0.96 | 0.95 | 0.97 | 0.97 |
Activity Data | Precision | Recall | F1-Score | |
---|---|---|---|---|
Vibrating Arm | 0 | 0.9 | 0.98 | 0.98 |
1 | 0.12 | 0.12 | 0.12 | |
Accuracy | 0.98 | |||
Micro Avg | 0.88 | 0.89 | 0.89 | 0.99 |
Weighted Avg | 0.98 | 0.99 | 0.98 | 0.97 |
Activity | Wagner Analysis [5] | Noury et al. [11] | Alessandra Moschetti et al. [12] | Smart Cane Method |
---|---|---|---|---|
Normal Walking | 100% | 91% | 90% | 99.7% |
Slow Walking | Not Evaluated | Not Evaluated | Not Evaluated | 99.6% |
Exhausted | Not Evaluated | Not Evaluated | Not Evaluated | 98.1% |
Vibrating Arm | Not Evaluated | Not Evaluated | Not Evaluated | 97.8% |
Resting | 91.6% | 91.2% | 87.8% | 97.6% |
Irregular Arm Swing | Not Evaluated | Not Evaluated | Not Evaluated | 89.9% |
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Adebiyi, M.O.; Abdulrasaq, S.; Olugbara, O. Abnormal Gait and Tremor Detection in the Elderly Ambulatory Behavior Using an IoT Smart Cane Device. BioMedInformatics 2022, 2, 528-543. https://doi.org/10.3390/biomedinformatics2040033
Adebiyi MO, Abdulrasaq S, Olugbara O. Abnormal Gait and Tremor Detection in the Elderly Ambulatory Behavior Using an IoT Smart Cane Device. BioMedInformatics. 2022; 2(4):528-543. https://doi.org/10.3390/biomedinformatics2040033
Chicago/Turabian StyleAdebiyi, Marion O., Surajudeen Abdulrasaq, and Oludayo Olugbara. 2022. "Abnormal Gait and Tremor Detection in the Elderly Ambulatory Behavior Using an IoT Smart Cane Device" BioMedInformatics 2, no. 4: 528-543. https://doi.org/10.3390/biomedinformatics2040033
APA StyleAdebiyi, M. O., Abdulrasaq, S., & Olugbara, O. (2022). Abnormal Gait and Tremor Detection in the Elderly Ambulatory Behavior Using an IoT Smart Cane Device. BioMedInformatics, 2(4), 528-543. https://doi.org/10.3390/biomedinformatics2040033